Linked Data - Connect Distributed Data across the Web

I recently had an email giving advance notice that a review of the Linked Data Book (aka “Linked Data: Evolving the Web into a Global Data Space“) would appear in Volume 11(2) of the Journal of Web Engineering, published by Rinton Press (ISSN: 1540-9589). As some people won’t have easy access to the journal, the review is republished here, with permission. It’s by Bebo White of Stanford University and beyond — thank you Bebo for the thoughtful review, and to Rinton Press for allowing it to be republished here.

Web Engineering has been described as encompassing those “technologies, methodologies, tools, and techniques used to develop and maintain Web-based applications leading to better systems, [thus to] enabling and improving the dissemination and use of content and services though the Web.” (Source: International Conference on Web Engineering)

An especially interesting aspect of this description is “dissemination and use of content.” Semantic Web technologies and particularly the Linked Data paradigm have evolved as powerful enablers for the transition of the current document-oriented Web into a Web of interlinked data/content and, ultimately, into the Semantic Web.

To facilitate this transition many aspects of distributed data and information management need to be adapted, advanced and integrated. Of particular importance are approaches for (1) extracting semantics from unstructured, semi-structured and existing structured sources, (2) management of large volumes of RDF data, (3) techniques for efficient automatic and semi-automatic data linking, (4) algorithms, tools, and inference techniques for repairing and enriching Linked Data with conceptual knowledge, (5) the collaborative authoring and creation of data on the Web, (6) the establishment of trust by preserving provenance and tracing lineage, (7) user-friendly means for browsing, exploration and search of large, federated Linked Data spaces. Particularly promising might be the synergistic combination of approaches and techniques touching upon several of these aspects at once.

For Web Engineering practitioners interested in being a part of this Web transition, Linked Data – Evolving the Web into a Global Data Space by Heath and Bizer will provide a valuable resource. The authors have done an excellent job of addressing the subject in a logical sequence of well-written chapters reflecting technical fundamentals, coverage of existing applications and tools, and the challenges for future development and research. The seven important approaches mentioned earlier are described in a consistent way and illustrated by means of a hypothetical scenario that evolves over the course of the book. The size of this book (122 pages) is deceiving in that it does not reflect the quality and density of its content. The authors have succeeded in presenting a complex topic both succinctly and clearly. It is not a “quick read,” but rather a volume to be used for references, definitions, and meaningful and instructive code examples.

This book is available in digital format (PDF). It is the first in a planned series of books/lectures. The quality of this book should make the reader/practitioner look forward to the upcoming series volumes that promise to further explain the exciting future of this topic.

Linked Data is Sometimes a Useful Technique, but is an Inadequate Focus

While in Australia on other business, I had the great fortune to be invited by Adam Bell of the Australian War Memorial to be the featured speaker at the Canberra Semantic Web Meetup on April 23. The talk was held within the impressive BAE Systems Theatre of the Memorial and was very well attended. My talk was preceded by an excellent introduction to the semantic Web by David Ratcliffe and Armin Haller of CSIRO.  They have kindly provided their useful slides online.

Many of the attendees came from the perspective of libraries, archives or museums. They naturally had an interest in the linked data activities in this area, a growing initiative that is now known under the acronym of LOD-LAM. Though I have been an advocate of linked data going back to 2006, one of my main theses was that linked data was an inadequate focus to achieve interoperability. The key emphases of my talk were that the pragmatic contributions of semantic technologies reside more in mindsets, information models and architectures than in ‘linked data’ as currently practiced.

Disappointments and Successes

The semantic Web and its most recent branding of linked data has antecedents going back to 1945 via Vannevar Bush’s memex and Ted Nelson’s hypertext of the early 1960s. The most powerful portrayal of the potential of the semantic Web comes in Douglas Adams’ 1990 Hyperland special for the BBC, a full decade before Tim Berners-Lee and colleagues first coined the term ‘semantic web’ [1]. The Hyperland vision of obsequious intelligent agents doing our very bidding has, of course, not been fully realized. The lack of visible uptake of this full vision has caused some proponents to back away from the idea of the semantic Web. Linked data, in fact, was a term coined by Berners-Lee himself, arguably in part to re-brand the idea and to focus on a more immediate, achievable vision. In its first formulation linked data emphasized the RDF (Resource Description Framework) data model, though others, notably Kingsley Idehen, have attempted to put forward a revisionist definition of linked data that includes any form of structured data involving entity attribute values (EAV).

No matter how expressed, the idea behind all of these various terms has in essence been to make meaningful connections, to provide the frameworks for interoperability. Interoperability means getting disparate sources of data to relate to each other, as a means of moving from data to information. Interoperability requires that source and receiver share a vocabulary about what things mean, as well as shared understandings about the associations or degree of relationship between the items being linked.

The current concept of linked data attempts to place these burdens mostly on the way data is published. While apparently “simpler” than earlier versions of the semantic Web (since linked data de-emphasizes shared vocabularies and nuanced associations), linked data places onerous burdens on how publishers express their data. Though many in the advocacy community point to the “billions” of RDF triples expressed as a success, actual consumers of linked data are rare. I know of no meaningful application or example where the consumption of linked data is an essential component.

However, there are a few areas of success in linked data. DBpedia, Freebase (now owned by Google), and GeoNames have been notable in providing identifiers (URIs) for common concepts, things, entities and places. There has also been success in the biomedical community with linked data.

Meanwhile, other aspects of the semantic Web have also shown success, but been quite hidden. Apple’s spoken Siri service is driven by an ontological back-end; schema.org is beginning to provide shared ways for tagging key entities and concepts, as promoted by the leading search engines of Google, Bing, Yahoo! and Yandex; Bing itself has been improved as a search service by the incorporation of the semantic search technologies of its earlier Powerset acquisition; and Google is further showing how NLP (natural language processing) techniques can be used to extract meaningful structure for characterizing entities in search results and in search completion and machine language translation. These services are here today and widely used. All operate in the background.

What Lessons Can We Derive?

These failures and successes help provide some pragmatic lessons going forward.

While I disagree with Kingsley’s revisionist approach to re-defining linked data, I very much agree with his underlying premise:  effective data exchange does not require RDF. Most instance records are already expressed as simple entity-value pairs, and any data transfer serialization — from key-value pairs to JSON to CSV spreadsheets — can be readily transformed to RDF.

Semantic technologies are fundamentally about knowledge representation, not data transfer.

This understanding is important because the fundamental contribution of RDF is not as a data exchange format, but as a foundational data model. The simple triple model of RDF can easily express the information assertions in any form of content, from completely unstructured text (after information extraction or metadata characterization) to the most structured data sources. Triples can themselves be built up into complete languages (such as OWL) that also capture the expressiveness necessary to represent any extant data or information schema [2].

The ability of RDF to capture any form of data or any existing schema makes it a “universal solvent” for information. This means that the real role of RDF is as a canonical data model at the core of the entire information architecture. Linked data, with its emphasis on data publishing and exchange, gets this focus exactly wrong. Linked data emphasizes RDF at the wrong end of the telescope.

The idea of common schema and representations is at the core of the semantic Web successes that do exist. In fact, when we look at Siri, emerging search, or some of the other successes noted above, we see that their semantic technology components are quite hidden. Successful semantics tend to work in the background, not in the foreground in terms of how data is either published or consumed. Semantic technologies are fundamentally about knowledge representation, not data transfer.

Where linked data is being consumed, it is within communities such as the life sciences where much work has gone into deriving shared vocabularies and semantics for linking and mapping data. These bases for community sharing express themselves as ontologies, which are really just formalized understandings of these shared languages in the applicable domain (life sciences, in this case). In these cases, curation and community processes for deriving shared languages are much more important to emphasize than how data gets exposed and published.

Linked data as presently advocated has the wrong focus. The techniques of publishing data and de-referencing URIs are given prominence over data quality, meaningful linkages (witness the appalling misuse of owl:sameAs [3]), and shared vocabularies. These are the reasons we see little meaningful consumption of linked data. It is also the reason that the much touted FYN (“follow your nose”) plays no meaningful information role today other than a somewhat amusing diversion.

Shifting the Focus

In our own applications Structured Dynamics promotes seven pillars to pragmatic semantic technologies [4]. Linked data is one of those pillars, because where the other foundations are in place, including shared understandings, linked data is the most efficient data transfer format. But, as noted, linked data alone is insufficient.

Linked data is thus the wrong starting focus for new communities and users wishing to gain the advantages of interoperability. The benefits of interoperability must first obtain from a core (or canonical) data model — RDF — that is able to capture any extant data or schema. As these external representations get boiled down to a canonical form, there must be shared understandings and vocabularies to capture the meaning in this information. This puts community involvement and processes at the forefront of the semantic enterprise. Only after the community has derived these shared understandings should linked data be considered as the most efficient way to interchange data amongst the community members.

Identifying and solving the “wrong” problems is a recipe for disappointment. The challenges of the semantic Web are not in branding or messaging. The challenges of the semantic enterprise and Web reside more in mindsets, approaches and architecture. Linked data is merely a technique that contributes little — perhaps worse by providing the wrong focus — to solving the fundamental issue of information interoperability.

Once this focus shifts, a number of new insights emerge. Structure is good in any form; arguments over serializations or data formats are silly and divert focus. The role of semantic technologies is likely to be a more hidden one, to reside in the background as current successes are now showing us. Building communities with trusted provenance and shared vocabularies (ontologies) are the essential starting points. Embracing and learning about NLP will be important to include the 80% of content currently in unstructured text and disambiguating reference conflicts. Ultimate users, subject matter experts and librarians are much more important contributors to this process than developers or computer scientists. We largely now have the necessary specifications and technologies in place; it is time for content and semantic reconciliation to guide the process.

It is great that the abiding interest in interoperability is leading to the creation of more and more communities, such as LOD-LAM, forming around the idea of linked data. What is important moving forward is to use these interests as springboards, and not boxes, for exploring the breadth of available semantic technologies.

For More on the Talk

Below is a link to my slides used in Canberra:

Pragmatic Approaches to the Semantic Web View more presentations from Mike Bergman.

Also, as mentioned, the intro slides are online, a video recording of the presentations is also available, and some other blog postings occasioned by the talks are also online.

[1] Tim Berners-Lee, James Hendler and Ora Lassila, 2001. “The Semantic Web”. Scientific American Magazine; see http://www.scientificamerican.com/article.cfm?id=the-semantic-web. [2] See further, M.K. Bergman, 2009. “Advantages and Myths of RDF,” AI3:::Adaptive Innovation blog, April 8, 2009. See http://www.mkbergman.com/483/advantages-and-myths-of-rdf/. [3] See, among many, M.K. Bergman, 2010. “Practical P-P-P-Problems with Linked Data,” AI3:::Adaptive Innovation blog, October 4, 2010. See http://www.mkbergman.com/917/practical-p-p-p-problems-with-linked-data/. [4] M.K. Bergman, 2010. “Seven Pillars of the Open Semantic Enterprise,” AI3:::Adaptive Innovation blog, January 12, 2010. See http://www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise/.

Adaptive Information is a Hammer, but Genes are Not a Nail

Since Richard Dawkins first put forward the idea of the “meme” in his book The Selfish Gene some 35 years ago [1], the premise has struck in my craw. I, like Dawkins, was trained as an evolutionary biologist. I understand the idea of the gene and its essential role as a vehicle for organic evolution. And, all of us clearly understand that “ideas” themselves have a certain competitive and adaptive nature. Some go viral; some run like wildfire and take prominence; and some go nowhere or fall on deaf ears. Culture and human communications and ideas play complementary — perhaps even dominant — roles in comparison to the biological information contained within DNA (genes).

I think there are two bases for why the “meme” idea sticks in my craw. The first harkens back to Dawkins. In formulating the concept of the “meme”, Dawkins falls into the trap of many professionals, what the French call déformation professionnelle. This is the idea of professionals framing problems from the confines of their own points of view. This is also known as the Law of the Instrument, or (Abraham) Maslow‘s hammer, or what all of us know colloquially as “if all you have is a hammer, everything looks like a nail“ [2]. Human or cultural information is not genetics.

The second — and more fundamental — basis for why this idea sticks in my craw is its mis-characterization of what is adaptive information, the title and theme of this blog. Sure, adaptive information can be found in the types of information structures at the basis of organic life and organic evolution. But, adaptive information is much, much more. Adaptive information is any structure that provides arrangements of energy and matter that maximizes entropy production. In inanimate terms, such structures include chemical chirality and proteins. It includes the bases for organic life, inheritance and organic evolution. For some life forms, it might include communications such as pheromones or bird or whale songs or the primitive use of tools or communicated behaviors such as nest building. For humans with their unique abilities to manipulate and communicate symbols, adaptive information embraces such structures as languages, books and technology artifacts. These structures don’t look or act like genes and are not replicators in any fashion of the term. To hammer them as “memes” significantly distorts their fundamental nature as information structures and glosses over what factors might — or might not — make them adaptive.

I have been thinking of these concepts much over the past few decades. Recently, though, there has been a spate of the “meme” term, particularly on the semantic Web mailing lists to which I subscribe. This spewing has caused me to outline some basic ideas about what I find so problematic in the use of the “meme” concept.

A Brief Disquisition on Memes

As defined by Dawkins and expanded upon by others, a “meme” is an idea, behavior or style that spreads from person to person within a culture. It is proposed as being able to be transmitted through writing, speech, gestures or rituals. Dawkins specifically called melodies, catch-phrases, fashion and the technology of building arches as examples of memes. A meme is postulated as a cultural analogue to genes in that they are assumed to be able to self-replicate, mutate or respond to selective pressures. Thus, as proposed, memes may evolve by natural selection in a manner analogous to that of biological evolution.

However, unlike a gene, a structure corresponding to a “meme” has never been discovered or observed. There is no evidence for it as a unit of replication, or indeed as any kind of coherent unit at all. In its sloppy use, it is hard to see how “meme” differs in its scope from concepts, ideas or any form of cultural information or transmission, yet it is imbued with properties analogous to animate evolution for which there is not a shred of empirical evidence.

One might say, so what, the idea of a “meme” is merely a metaphor, what is the harm? Well, the harm comes about when it is taken seriously as a means of explaining human behavior and cultural changes, a field of study called memetics. It becomes a pseudo-scientific term that sets a boundary condition for understanding the nature of information and what makes it adaptive or not [3]. Mechanisms and structures appropriate to animate life are not universal information structures, they are simply the structures that have evolved in the organic realm. In the human realm of signs and symbols and digital information and media, information is the universal, not the genetic structure of organic evolution.

The noted evolutionary geneticist, R.C. Lewontin, one of my key influences as a student, has also been harshly critical of the idea of memetics [4]:

 ”The selectionist paradigm requires the reduction of society and culture to inheritance systems that consist of randomly varying, individual units, some of which are selected, and some not; and with society and culture thus reduced to inheritance systems, history can be reduced to ‘evolution.’ . . . we conclude that while historical phenomena can always be modeled selectionistically, selectionist explanations do not work, nor do they contribute anything new except a misleading vocabulary that anesthetizes history.”

Consistent with my recent writings about Charles S. Peirce [5], many logicians and semiotic theorists are also critical of the idea of “memes”, but on different grounds. The criticism here is that “memes” distort Peirce’s ideas about signs and the reification of signs and symbols via a triadic nature. Notable in this camp is Terrence Deacon [6].

Information is a First Principle

It is not surprising that the concept of “memes” arose in the first place. It is understandable to seek universal principles consistent with natural laws and observations. The mechanism of natural evolution works on the information embodied in DNA, so why not look to genes as some form of universal model?

The problem here, I think, was to confuse mechanisms with first principles. Genes are a mechanism — a “structure” if you will — that along with other forms of natural selection such as the entire organism and even kin selection [7], have evolved as means of adaptation in the animate world. But the fundamental thing to be looked for here is the idea of information, not the mechanism of genes and how they replicate. The idea of information holds the key for drilling down to universal principles that may find commonality between information for humans in a cultural sense and information conveyed through natural evolution for life forms. It is the search for this commonality that has driven my professional interests for decades, spanning from population genetics and evolution to computers, information theory and semantics [8].

But before we can tackle these connections head on, it is important to address a couple of important misconceptions (as I see them).

Seque #1: Information is (Not!) Entropy

In looking to information as a first principle, Claude Shannon‘s seminal work in 1948 on information theory must be taken as the essential point of departure [9]. The motivation of Shannon’s paper and work by others preceding him was to understand information losses in communication systems or networks. Much of the impetus for this came about because of issues in wartime communications and early ciphers and cryptography. (As a result, the Shannon paper is also intimately related to data patterns and data compression, not further discussed here.)

In a strict sense, Shannon’s paper was really talking about the amount of information that could be theoretically and predictably communicated between a sender and a receiver. No context or semantics were implied in this communication, only the amount of information (for which Shannon introduced the term “bits” [10]) and what might be subject to losses (or uncertainty in the accurate communication of the message). In this regard, what Shannon called “information” is what we would best term “data” in today’s parlance.

The form that the uncertainty (unpredictability) calculation that Shannon derived:

very much resembled the mathematical form for Boltzmann‘s original definition of entropy (as elaborated upon by Gibbs, denoted as S, for Gibb’s entropy):

and thus Shannon also labelled his measure of unpredictability, H, as entropy [10].

After Shannon, and nearly a century after Boltzmann, work by individuals such as Jaynes in the field of statistical mechanics came to show that thermodynamic entropy can indeed be seen as an application of Shannon’s information theory, so there are close parallels [11]. This parallel of mathematical form and terminology has led many to assert that information is entropy.

I believe this assertion is a misconception on two grounds.

First, as noted, what is actually being measured here is data (or bits), not information embodying any semantic meaning or context. Thus, the formula and terminology is not accurate for discussing “information” in a conventional sense.

Second, the Shannon methods are based on the communication (transmittal) between a sender and a receiver. Thus the Shannon entropy measure is actually a measure of the uncertainty for either one of these states. The actual information that gets transmitted and predictably received was formulated by Shannon as R (which he called rate), and he expressed basically as:

R = Hbefore – Hafter

R, then, becomes a proxy for the amount of information accurately communicated. R can never be zero (because all communication systems have losses). Hbefore and Hafter are both state functions for the message, so this also makes R a function of state. So while there is Shannon entropy (unpredictability) for any given sending or receiving state, the actual amount of information (that is, data) that is transmitted is a change in state as measured by a change in uncertainty between sender (Hbefore) and receiver (Hafter). In the words of Thomas Schneider, who provides a very clear discussion of this distinction [12]:

Information is always a measure of the decrease of uncertainty at a receiver.

These points do not directly bear on the basis of information as discussed below, but help remove misunderstandings that might undercut those points. Further, these clarifications make consistent theoretical foundations of information (data) with natural evolution while being logically consistent with the 2nd law of thermodynamics (see next).

Seque #2: Entropy is (Not!) Disorder

The 2nd law of thermodynamics expresses the tendency that, over time, differences in temperature, pressure, or chemical potential equilibrate in an isolated physical system. Entropy is a measure of this equilibration: for a given physical system, the highest entropy state is one at equilibrium. Fluxes or gradients arise when there are differences in state potentials in these systems. (In physical systems, these are known as sources and sinks; in information theory, they are sender and receiver.) Fluxes go from low to high entropy, and are non-reversible — the “arrow of time” — without the addition of external energy. Heat, for example, is a by product of fluxes in thermal energy. Because these fluxes are directional in isolation, a perpetual motion machine is shown as impossible.

In a closed system (namely, the entire cosmos), one can see this gradient as spanning from order to disorder, with the equilibrium state being the random distribution of all things. This perspective, and much schooling regarding these concepts, tends to present the idea of entropy as a “disordered” state. Life is seen as the “ordered” state in this mindset. Hewing to this perspective, some prominent philosophers, scientists and others have sometimes tried to present the “force” representing life and “order” as an opposite one to entropy. One common term for this opposite “force” is “negentropy” [13].

But, in the real conditions common to our lives, our environment is distinctly open, not closed. We experience massive influxes of energy via sunlight, and have learned as well how to harness stored energy from eons past in further sources of fossil and nuclear energy. Our open world is indeed a high energy one, and one that increases that high-energy state as our knowledge leads us to exploit still further resources of higher and higher quality. As Buckminster Fuller once famously noted, electricity consumption (one of the highest quality energy resources found to date) has become a telling metric about the well-being and wealth of human societies [14].

The high-energy environments fostering life on earth and more recently human evolution establish a local (in a cosmic sense) gradient that promotes fluxes to more ordered states, not lesser unordered ones. These fluxes remain faithful to basic physical laws and are non-deterministic [15]. Indeed, such local gradients can themselves be seen as consistent with the conditions initially leading to life, favoring the random event in the early primordial soup that led to chemical structures such as chirality, auto-catalytic reactions, enzymes, and then proteins, which became the eventual building blocks for animate life [16].

These events did not have preordained outcomes (that is, they were non-deterministic), but were the result of time and variation in the face of external energy inputs to favor the marginal combinatorial improvement. The favoring of the new marginal improvement also arises consistent with entropy principles, by giving a competitive edge to those structures that produce faster movements across the existing energy gradient. According to Annila and Annila [16]:

“According to the thermodynamics of open systems, every entity, simple or sophisticated, is considered as a catalyst to increase entropy, i.e., to diminish free energy. Catalysis calls for structures. Therefore, the spontaneous rise of structural diversity is inevitably biased toward functional complexity to attain and maintain high-entropy states.”

Via this analysis we see that life is not at odds with entropy, but is consistent with it. Further, we see that incremental improvements in structure that are consistent with the maximum entropy production principle will be favored [17]. Of course, absent the external inputs of energy, these gradients would reverse. Under those conditions, the 2nd law would promote a breakdown to a less ordered system, what most of us have been taught in schools.

With these understandings we can now see the dichotomy as life representing order with entropy disorder as being false. Further, we can see a guiding set of principles that is consistent across the broad span of evolution from primordial chemicals and enzymes to basic life and on to human knowledge and artifacts. This insight provides the fundamental “unit” we need to be looking toward, and not the gene nor the “meme”.

Information is Structure

Of course, the fundamental “unit” we are talking about here is information, and not limited as is Shannon’s concept to data. The quality that changes data to information is structure, and structure of a particular sort. Like all structure, there is order or patterns, often of a hierarchical or fractal or graph nature. But the real aspect of the structure that is important is the marginal ability of that structure to lead to improvements in entropy production. That is, processes are most adaptive (and therefore selected) that maximize entropy production. Any structure that emerges that is able to reduce the energy gradient faster will be favored.

However, remember, these are probabilistic, statistical processes. Uncertainties in state may favor one structure at one time versus another at a different time. The types of chemical compounds favored in the primordial soup were likely greatly influenced by thermal and light cycles and drying and wet conditions. In biological ecosystems, there are huge differences in seed or offspring production or in overall species diversity and ecological complexity based on the stability (say, tropics) or instability (say, disturbance) of local environments. As noted, these processes are inherently non-deterministic.

As we climb up the chain from the primordial ooze to life and then to humans and our many information mechanisms and technology artifacts (which are themselves embodiments of information), we see increasing complexity and structure. But we do not see uniformity of mechanisms or vehicles.

The general mechanisms of information transfer in living organisms occur (generally) via DNA in genes, mediated by sex in higher organisms, subject to random mutations, and then kept or lost entirely as their host organisms survive to procreate or not. Those are harsh conditions: the information survives or not (on a population basis) with high concentrations of information in DNA and with a priority placed on remixing for new combinations via sex. Information exchange (generally) only occurs at each generational event.

Human cultural information, however, is of an entirely different nature. Information can be made persistent, can be recorded and shared across individuals or generations, extended with new innovations like written language or digital computers, or combined in ways that defy the limits of sex. Occasionally, of course, loss of living languages due to certain cultures or populations dying out or horrendous catastrophes like the Spanish burning (nearly all of) the Mayan’s existing books can also occur [18]. The environment will also be uncertain.

So, while we can define DNA in genes or the ideas of a “meme” all as information, in fact we now see how very unlike the dynamics and structures of these two forms really are. We can be awestruck with the elegance and sublimity of organic evolution. We can also be inspired by song or poem or moved to action through ideals such as truth and justice. But organic evolution does not transpire like reading a book or hearing a sermon, just like human ideas and innovations don’t act like genes. The “meme” is a totally false analogy. The only constant is information.

Some Tentative Implications

The closer we come to finding true universals, the better we will be able to create maximum entropy producing structures. This, in turn, has some pretty profound implications. The insight that keys these implications begins with an understanding of the fundamental nature — and importance — of information. According to Karnani et al [19]:

“. . . the common contemporary consent, the second law of thermodynamics, is perceived to drive disorder. Therefore, it may appear, at first sight, inconceivable that this universal law could possibly account for the existence and orderly characteristics of information, as well as for its meaningful content. However, the second law, or equivalently the principle of increasing entropy, merely states that difference among energy densities tends to vanish. When the surrounding energy density is high, the system will evolve toward a stationary state by increasing its energy content, e.g, by devising orderly machinery for energy transduction to acquire energy. . . . Syntax of information, when described by thermodynamics, is associated with the entropy of the physical representation, and significance of information is associated with the entropy increase in the receiver system when it executes the encoded information.”

All would agree that the evolution of life over the past few billion years is truly wondrous. But, what is equally wondrous is that the human species has come to learn and master symbols. That mastery, in turn, has broken the bounds of organic evolution and has put into our hands the very means and structure of information itself. Via this entirely new — and incredibly accelerated — path to information structures, we are only now beginning to see some of its implications:

• Unlike all other organisms, we dominate our environment and have experienced increasing wealth and freedom. Wealth increases and their universal applicability continue to increase at an exponential rate [20]
• We no longer depend on the random variant to maximize our entropy producing structures. We can now do so purposefully and with symbologies and bit streams of our own devising
• Potentially all information variants can be recorded and shared across all human individuals and generations, a complete decoupling from organic boundaries
• Key ideas and abstractions, such as truth, justice and equality, can operate on a species-wide basis and become adopted without massive die-offs of individuals
• We are actively moving ourselves into higher-level energy states, further increasing the potential for wealth and new structures
• We are actively impacting our local environment, potentially creating the conditions for our species’ demise
• We are increasingly engaging all individuals of the human species in these endeavors through literacy, education and access to global information sources. This provides a still further multiplier effect on humanity’s ability to devise and manipulate information structures into more adaptive and highly-ordered states.

The idea of a “meme” actually cheapens our understanding of these potentials.

Ideas matter and terminology matters. These are the symbols by which we define and communicate potentials. If we choose the wrong analogies or symbols — as “meme” is in this case — we are picking the option with the lower entropy potential. Whether I assert it to be so or not, the “meme” concept is an information structure doomed for extinction.

[1] Richard Dawkins, 1976. The Selfish Gene, Oxford University Press, New York City, ISBN 0-19-286092-5. [2] This phrase was perhaps first made famous by Mark Twain or Bernard Baruch, but in any case is clearly understood now by all. [3] According to Wikipedia, Benitez-Bribiesca calls memetics “a dangerous idea that poses a threat to the serious study of consciousness and cultural evolution”. He points to the lack of a coding structure analogous to the DNA of genes, and to instability of any mutation mechanisms for “memes” sufficient for standard evolution processes. See Luis Benitez Bribiesca, 2001. “Memetics: A Dangerous Idea”, Interciencia: Revista de Ciencia y Technologia de América (Venezuela: Asociación Interciencia) 26 (1): 29–31, January 2001. See http://redalyc.uaemex.mx/redalyc/pdf/339/33905206.pdf. [4] Joseph Fracchia and R.C. Lewontin, 2005. “The Price of Metaphor”, History and Theory (Wesleyan University) 44 (44): 14–29, February 2005. [5] See further M. K. Bergman, 2012. “Give Me a Sign: What Do Things Mean on the Semantic Web?,” posting on AI3:::Adaptive Information blog, January 24, 2012. See http://www.mkbergman.com/994/give-me-a-sign-what-do-things-mean-on-the-semantic-web/. [6] Terrence Deacon, 1999. “The Trouble with Memes (and what to do about it)”. The Semiotic Review of Books 10(3). See http://projects.chass.utoronto.ca/semiotics/srb/10-3edit.html. [7] Kin selection refers to changes in gene frequency across generations that are driven at least in part by interactions between related individuals. Some mathematical models show how evolution may favor the reproductive success of an organism’s relatives, even at a cost to an individual organism. Under this mode, selection can occur at the level of populations and not the individual or the gene. Kin selection is often posed as the mechanism for the evolution of altruism or social insects. Among others, kin selection and inclusive fitness was popularized by W. D. Hamilton and Robert Trivers. [8] You may want to see my statement of purpose under the Blogasbörd topic, first written seven years ago when I started this blog. [9] Claude E. Shannon, 1948. “A Mathematical Theory of Communication”, Bell System Technical Journal, 27: 379–423, 623-656, July, October, 1948. See http://cm.bell-labs.com/cm/ms/what/shannonday/shannon1948.pdf. [10] As Shannon acknowledges in his paper, the “bit” term was actually suggested by J. W. Tukey. Shannon can be more accurately said to have popularized the term via his paper. [11] See further http://en.wikipedia.org/wiki/Information_entropy#Relationship_to_thermodynamic_entropy. [12] See Thomas D. Schneider, 2012. “Information Is Not Entropy, Information Is Not Uncertainty!,” Web page retrieved April 4, 2012; see http://www.lecb.ncifcrf.gov/~toms/information.is.not.uncertainty.html. [13] The “negative entropy” (also called negentropy or syntropy) of a living system is the entropy that it exports to keep its own entropy low, and according to proponents lies at the intersection of entropy and life. The concept and phrase “negative entropy” were introduced by Erwin Schrödinger in his 1944 popular-science book What is Life?. See Erwin Schrödinger, 1944. What is Life – the Physical Aspect of the Living Cell, Cambridge University Press, 1944. A copy may be downloaded at http://old.biovip.com/UpLoadFiles/Aaron/Files/2005051204.pdf. [14] R. Buckminster Fuller, 1981. Critical Path, St. Martin’s Press, New York City, 471 pp. See especially p. 103 ff. [15] The seminal paper first presenting this argument is Vivek Sharma and Arto Annila, 2007. “Natural Process – Natural Selection”, Biophysical Chemistry 127: 123-128. See http://www.helsinki.fi/~aannila/arto/natprocess.pdf. This basic theme has been much expanded upon by Annila and his various co-authors. See, for example, [16] and [19], among many others. [16] Arto Annila and Erkki Annila, 2008. “Why Did Life Emerge?,” International Journal of Astrobiology 7(3 and 4): 293-300. See http://www.helsinki.fi/~aannila/arto/whylife.pdf. [17] According to Wikipedia, the principle (or “law”) of maximum entropy production is an aspect of non-equilibrium thermodynamics, a branch of thermodynamics that deals with systems that are not in thermodynamic equilibrium. Most systems found in nature are not in thermodynamic equilibrium and are subject to fluxes of matter and energy to and from other systems and to chemical reactions. One fundamental difference between equilibrium thermodynamics and non-equilibrium thermodynamics lies in the behavior of inhomogeneous systems, which require for their study knowledge of rates of reaction which are not considered in equilibrium thermodynamics of homogeneous systems. Another fundamental difference is the difficulty in defining entropy in macroscopic terms for systems not in thermodynamic equilibrium. The principle of maximum entropy production states that the in comparing two or more alternate paths for crossing an energy gradient that the one that creates the maximum entropy change will be favored. The maximum entropy (sometimes abbreviated MaxEnt or MaxEp) concept is related to this notion. It is also known as the maximum entropy production principle, or MEPP. [18] The actual number of Mayan books burned by the Spanish conquistadors is unknown, but is somewhere between tens and thousands; see here. Only three or four codexes are known to survive today. Also, Wikipedia contains a listing of notable book burnings throughout history. [19] Mahesh Karnani, Kimmo Pääkkönen and Arto Annila, 2009. “The Physical Character of Information,” Proceedings of the Royal Society A, April 27, 2009. See http://www.helsinki.fi/~aannila/arto/natinfo.pdf. [20] I discuss and chart the exponential growth of human wealth based on Angus Maddison data in M. K. Bergman, 2006. “The Biggest Disruption in History: Massively Accelerated Growth Since the Industrial Revolution,” post in AI3:::Adaptive Information blog, July 27, 2006. See http://www.mkbergman.com/250/the-biggest-disruption-in-history-massively-accelerated-growth-since-the-industrial-revolution/.

Casting My Vote on Revising httpRange-14

The httpRange-14 issue and its predecessor “identity crisis” debate have been active for more than a decade on the Web [1]. It has been around so long that most acknowledge “fatigue” and it has acquired that rarified status as a permathread. Many want to throw up their hands when they hear of it again and some feel — because of its duration and lack of resolution — that there never will be closure on the question. Yet everyone continues to argue and then everyone wonders why actual consumption of linked data remains so problematic.

Jonathan Rees is to be thanked for refusing to let this sleeping dog lie. This issue is not going to go away so long as its basis and existing prescriptions are, in essence, incoherent. As a member of the W3C’s TAG (Technical Architecture Group), Rees has worked diligently to re-surface and re-frame the discussion. While I don’t agree with some of the specifics and especially with the constrained approach proposed for resolving this question [2], the sleeping dog has indeed been poked and is awake. For that we can thank Jonathan. Maybe now we can get it right and move on.

I don’t agree with how this issue has been re-framed and I don’t agree that responses to it must be constrained to the prescriptive approach specified in the TAG’s call for comments. Yet, that being said, as someone who has been vocal for years about the poor semantics of the semantic Web community, I feel I have an obligation to comment on this official call.

Thus, I am casting my vote behind David Booth’s alternative proposal [3], with one major caveat. I first explain the caveat and then my reasons for supporting Booth’s proposal. I have chosen not to submit a separate alternative in order to not add further to the noise, as Bernard Vatant (and, I’m sure, many, many others) has chosen [4].

Bury the Notion of ‘Information Resource’ Once and for All

I first commented on the absurdity of the ‘information resource’ terminology about five years ago [5]. Going back to Claude Shannon [6] we have come to understand information as entropy (or, more precisely, as differences in energy state). One need not get that theoretical to see that this terminology is confusing. “Information resource” is a term that defies understanding (meaning) or precision. It is also a distinction that leads to a natural counter-distinction, the “non-information resource”, which is also an imprecise absurdity.

What the confusing term is meant to encompass is web-accessible content (“documents”), as opposed to descriptions of (or statements about) things. This distinction then triggers a different understanding of a URI (locator v identifier alone) and different treatments of how to process and interpret that URI. But the term is so vague and easily misinterpreted that all of the guidance behind the machinery to be followed gets muddied, too. Even in the current chapter of the debate, key interlocutors confuse and disagree as to whether a book is an “information resource” or not. If we can’t basically separate the black balls from the white balls, how are we to know what to do with them?

If there must be a distinction, it should be based on the idea of the actual content of a thing — or perhaps more precisely web-accessible content or web-retrievable content — as opposed to the description of a thing. If there is a need to name this class of content things (a position that David Booth prefers, pers. comm.), then let’s use one of these more relevant terms and drop “information resource” (and its associated IR and NIR acronyms) entirely.

The motivation behind the “information resource” terminology also appears to be a desire that somehow a URI alone can convey the name of what a thing is or what it means. I recently tried to blow this notion to smithereens by using Peirce’s discussion of signs [1]. We should understand that naming and meaning may only be provided by the owner of a URI through additional explication, and then through what is understood by the recipient; the string of the URI itself conveys very little (or no) meaning in any semantic sense.

We should ban the notion of “information resource” forever. If the first exposure a potential new publisher or consumer of linked data encounters is “information resource”, we have immediately lost the game. Unresolvable abstractions lead to incomprehension and confusion.

The approach taken by the TAG in requesting new comments on httpRange-14 only compounds this problem. First, the guidance is to not allow any questioning of the “information resource” terminology within the prescribed comment framework [7]. Then, in the suggested framework for response, still further terminology such as “probe URIs”, “URI documentation carrier” or “nominal URI documentation carrier for a URI” is introduced. Aaaaarggghh! This only furthers the labored and artificial terminology common to this particular standards effort.

While Booth’s proposal does not call for an outright rejection of the “information resource” terminology (my one major qualification in supporting it), I like it because it purposefully sidesteps the question of the need to define “information resource” (see his Section 2.7). Booth’s proposal is also explicit in its rejection of implied meaning in URIs and through embrace of the idea of a protocol. Remember, all that is being put forward in any of these proposals is a mechanism for distinguishing between retrievable content obtainable at a given URL and a description of something found at a URI. By racheting down the implied intent, Booth’s proposal is more consistent with the purpose of the guidance and is not guilty of overreach.

Keep It Simple

One of the real strengths of Booth’s proposal is its rejection of the prescriptive method proposed by the TAG for suggesting an alternative to httpRange-14 [7]. The parsimonious objective should be to be simple, be clear, and be somewhat relaxed in terms of mechanisms and prescriptions. I believe use patterns — negotiated via adoption between publishers and consumers — will tell us over time what the “right” solutions may be.

Amongst the proposals put forward so far, David Booth’s is the most “neutral” with respect to imposed meanings or mechanisms, and is the simplest. Though I quibble in some respects, I offer qualified support for his alternative because it:

• Sidesteps the “information resource” definition (though weaker than I would want; see above)
• Addresses only the specific HTTP and HTTPS cases
• Avoids the constrained response format suggested by the TAG
• Explicitly rejects assigning innate meanings to URIs
• Poses the solution as a protocol (an understanding between publisher and consumer) rather than defining or establishing a meaning via naming
• Provides multiple “cow paths” by which resource definitions can be conveyed, which gives publishers and consumers choice and offers the best chance for more well-trodden paths to emerge
• Does not call for an outright repeal of the httpRange-14 rule, but retains it as one of multiple options for URI owners to describe resources
• Permits the use of an HTTP 200 response with RDF content as a means of conveying a URI definition
• Retains the use of the hash URI as an option
• Provides alternatives to those who can not easily (or at all) use the 303 see also redirect mechanism, and
• Simplifies the language and the presentation.

I would wholeheartedly support this approach were two things to be added: 1) the complete abandonment of all “information resource” terminology; and 2) an official demotion of the httpRange-14 rule (replacing it with a slash 303 option on equal footing to other options), including a disavowal of the “information resource” terminology. I suspect if the TAG adopts this option, that subsequent scrutiny and input might address these issues and improve its clarity even further.

There are other alternatives submitted, prominently the one by Jeni Tennison with many co-signatories [8]. This one, too, embraces multiple options and cow paths. However, it has the disadvantage of embedding itself into the same flawed terminology and structure as offered by httpRange-14.

[1] For my recent discussion about the history of these issues, see M.K. Bergman, 2012. “Give Me a Sign: What Do Things Mean on the Semantic Web?,” in AI3:::Adaptive Information blog, January 24, 2012; see http://www.mkbergman.com/994/give-me-a-sign-what-do-things-mean-on-the-semantic-web/. [2] In all fairness, this call was the result of ISSUE-57, which had its own constraints. Not knowing all of the background that led to the httpRange-14 Pandora’s Box being opened again, the benefit of the doubt would be that the form and approach prescribed by the TAG dictated the current approach. In any event, now that the Box is open, all pertinent issues should be addressed and the form of the final resolution should also not be constrained from what makes best sense and is most pragmatic. [3] David Booth‘s alternative proposal is for the “URI Definition and Discovery Protocol” (uddp). The actual submission according to form is found here. [4] See Bernard Vatant, 2012. “Beyond httpRange-14 Addiction,” the wheel and the hub blog, March 27, 2012. See http://blog.hubjects.com/2012/03/beyond-httprange-14-addiction.html. [5] M.K. Bergman, 2007. “More Structure, More Terminology and (hopefully) More Clarity,” in AI3:::Adaptive Information blog, July 27, 2007; see http://www.mkbergman.com/391/more-structure-more-terminology-and-hopefully-more-clarity/. Subsequent to that piece, I have written further on semantic Web semantics in “The Semantic Web and Industry Standards” (January 26, 2008), ” “The Shaky Semantics of the Semantic Web” (March 12, 2008), “Semantic Web Semantics: Arcane, but Important,” (April 8, 2008), “Context” href=”../440/the-semantics-of-context/”>The Semantics of Context,” (May 6, 2008), “When Linked Data Rules Fail” (November 16, 2009), “The Semantic ‘Gap’” (October 24, 2010) and [1]. [6] Claude E. Shannon, 1948. “A Mathematical Theory of Communication,” Bell System Technical Journal, Vol. 27, pp. 379–423, 623–656, 1948. [7] In the “Call for proposals to amend the “httpRange-14 resolution” (February 29, 2012), Jonathan Rees (presumably on behalf of the TAG), stated this as one of the rules of engagement: “9. Kindly avoid arguing in the change proposals over the terminology that is used in the baseline document. Please use the terminology that it uses. If necessary discuss terminology questions on the list as document issues independent of the 303 question.” The specific template formfor alternative proposals was also prescribed. In response to interactions on this question on the mailing list, Jonathan stated:

If it were up to me I’d purge “information resource” from the document, since I don’t want to argue about what it means, and strengthen the (a) clause to be about content or instantiation or something. But the document had to reflect the status quo, not things as I would have liked them to be. I have not submitted this as a change proposal because it doesn’t address ISSUE-57, but it is impossible to address ISSUE-57 with a 200-related change unless this issue is addressed, as you say, head on. This is what I’ve written in my TAG F2F preparation materials. [8] Jeni Tennison, 2012. “httpRange-14 Change Proposal,” submitted March 25, 2012. See the mailing list notice and actual proposal.

Phenomenal Growth in Less than Two Years

Today, for the first time, we passed 400 articles published on the open semantic framework (OSF) TechWiki. The TechWiki content is a baseline “starter kit” of documentation related to these OSF  projects and their contexts:

• conStruct – connecting modules to enable structWSF and sComponents to be hosted/embedded in Drupal
• structWSF – platform-independent suite of more than 20 RESTful Web services, organized for managing structured data datasets
• Semantic Components – JavaScript or Flex semantic components (widgets) for visualizing and manipulating structured data
• irON – instance record Object Notation for conveying XML, JSON or spreadsheets (CSV) in RDF-ready form, and
• Various parsers and standard data exchange formats and schema to facilitate information flow amongst these options.

The TechWiki covers all aspects of this open source OSF software stack. Besides the specific components developed and maintained by Structured Dynamics as listed above, the OSF stack combines many leading third-party software packages — such as Drupal for content management, Virtuoso for (RDF) triple storage, Solr for full-text indexing, GATE for natural language processing, the OWL API for ontology management, and others.

The TechWiki is the one-stop resource for how to install, configure, use and maintain these components. The best entry point to the OSF content on the TechWiki is represented by this entry page covering overall workflows in use of the system:

Since our first release of the TechWiki in July 2010, we have been publishing and releasing content steadily. We post a new article about every 1.5 calendar days, or about one per working day. This content is well-organized into (at present) 72 categories and is supported by nearly 500 figures and diagrams. Users are free to download and use this content at will, solely by providing attribution. The content has proven to be a goldmine for local use and modification by our clients, and for training and curriculum development.

The TechWiki represents a part of our commitment that we are successful when our customers no longer need us. As one of our most popular Web sites with fantastic and growing user stats, we invite you to visit and see what it means to provide open source semantic technologies as a total open solution.

Ontology-driven Application Meshes Structured Data with Public APIs

Locational information — points of interest/POIs, paths/routes/polylines, or polygons/regions — is common to many physical things in our real world. Because of its pervasiveness, it is important to have flexible and powerful display widgets that can respond to geo-locational data. We have been working for some time to extend our family of semantic components [1] within the open semantic framework (OSF) [2] to encompass just such capabilities. Structured Dynamics is thus pleased to announce that we have now added the sWebMap component, which marries the entire suite of Google Map API capabilities to the structured data management arising from the structWSF Web services framework [3] at the core of OSF.

The sWebMap component is fully in keeping with our design premise of ontology-driven applications, or ODapps [4]. The sWebMap component can itself be embedded in flexible layouts — using Drupal in our examples below — and can be very flexibly themed and configured. sWebMap we believe will rapidly move to the head of the class as the newest member of Structured Dynamics’ open source semantic components.

The absolutely cool thing about sWebMap is it just works. All one needs to do is relate it to a geo-enabled Search structWSF endpoint, and then all of the structured data with geo-locational attributes and its facets and structure becomes automagically available to the mapping widget. From there you can flexible map, display, configure, filter, select and keep those selections persistent and share with others. As new structured data is added to your system, that data too becomes automatically available.

Key Further Links

Though screen shots in the operation of this component are provided below, here are some further links to learn more:

• Live demo of the sWebMap component on the Citizen Dan sandbox
• An explanation of some of the technical details behind the sWebMap component
• sWebMap documentation on the TechWiki, including configuration and styling options
• General background information on ODApps, ontology-driven applications.

sWebMap Overview

There is considerable functionality in the sWebMap widget, not all immediately obvious when you first view it.

NOTE: a wide variety of configuration options — icons and colors — matched with the specific data and base tiling maps appropriate to a given installation may produce maps of significantly different aspect from the screenshots presented below. Click on any screenshot to get a full-size view.

Here is an example for sWebMap when it first comes up, using an example for the “Beaumont neighborhood”:

views

It is possible to set pre-selected items for any map display. That was done in this case, which shows the pre-selected items and region highlighted on the map and in the records listing (lower left below map).

The basic layout of the map has its main search options at the top, followed by the map itself and then two panels underneath:

The left-hand panel underneath the map presents the results listing. The right-hand panel presents the various filter options by which these results are generated. The filter options consist of:

• Sources – the datasets available to the instance
• Kinds – the kinds or types of data (owl:Classes or rdf:types) contained within those datasets, and
• Attributes – the specific attributes and their values for those kinds or sources.

As selections are made in sources or kinds, the subsequent choices narrow.

The layout below shows the key controls available on the sWebMap:

You can go directly to an affiliated page by clicking the upper right icon. This area often shows a help button or other guide. The search box below that enables you to search for any available data in the system. If there is information that can be mapped AND which occurs within the viewport of the current map size, those results will appear as one of three geographic feature types on the map:

• Markers, which can be configured with differing icons for specific types or kinds of data
• Polylines, such as highways or bus routes, or
• Polygons, which enclose specific regions on the map through a series of drawn points in a closed area.

At the map’s right is the standard map control that allows you to scroll the map area or zoom. Like regular Google maps, you can zoom (+ or – keys, or middle wheel on mouse) or navigate (arrow direction keys, or left mouse down and move) the map.

Current records are shown below the map. Specific records may be selected with its checkbox; this keeps them persistent on the map and in the record listing no matter what the active filter conditions may be. (You may also see a little drawing icon [], which presents an attribute report — similar to a Wikipedia ‘infobox‘ — for the current record). You can see in this case that the selected record also corresponds to a region (polygon) shape on the map.

sWebMap Views, Layers and Layouts

In the map area itself, it is possible to also get different map views by selecting one of the upper right choices. In this case, we can see a satellite view (or “layer”):

Or, we can choose to see a terrain layer:

Or there may optionally be other layers or views available in this same section.

Another option that appears on the map is the ability to get a street view of the map. That is done by grabbing the person icon at the map left and dragging it to where you are interested within the map viewport. That also causes the street portion to be highlighted, with street view photos displayed (if they exist for that location):

By clicking the person icon again, you then shift into walking view:

Via the mouse, you can now navigate up and down these streets and change perspective to get a visual feel for the area.

Multi-map View

Another option you may invoke is the multi-map view of the sWebMap. In this case, the map viewing area expands to include three sub-maps under the main map area. Each sub-map is color-coded and shown as a rectangle on the main map. (This particular example is displaying assessment parcels for the sample instance.) These rectangles can be moved on the main map, in which case their sub-map displays also move:

You must re-size using the sub-map (which then causes the rectangle size to change on the main map). You may also pan the sub-maps (which then causes the rectangle to move on the main map). The results list at the lower left is determined by which of the three sub-maps is selected (as indicated by the heavier bottom border). 

Searching and Filter Selections

There are two ways to get filter selection details for your current map: Show All Records or Search.

NOTE: for all data and attributes as described below, only what is visible on the current map view is shown under counts or records. Counts and records change as you move the map around.

In the first case, we pick the Show All Records option at the bottom of the map view, which then brings up the detailed filter selections in the lower-right panel:

Here are some tips for using the left-hand records listing:

• If there are more than 10 records, pagination appears at the bottom of the listing
• Each record is denoted by an icon for the kind of thing it is (bus stops v schools v golf courses, for example)
• If we mouse over a given record in the listing, its marker icon on the map bounces to show where it resides
• To the right of each record listing, the checkbox indicates whether you want the record to be maintained persistently. If you check it, the icon on the map changes color, the record is promoted to the top of the list where it becomes sticky and is given an alphabetic sequence. Unchecking this box undoes all of these changes
• To the right of each record listing is also the view record [] icon; clicking it shows the raw attribute data for that record.

The records that actually appear on this listing are based on the records scope or Search (see below) conditions, as altered by the filter settings on the right-hand listing under the sWebMap. For example, if we now remove the neighborhood record as being persistent and Show included records we now get items across the entire map viewport:

Search works in a similar fashion, in that it invokes the filter display with the same left- and right-hand listings appear under the sWebMap, only now only for those records that met the search conditions. (The allowable search syntax is that for Lucene.) Here is the result of a search, in this case for “school”:

As shown above, the right-hand panel is split into three sections: Sources (or datasets), Kinds (that is, similar types of things, such as bus stops v schools v golf courses), and Attributes (that is, characteristics for these various types of things). All selection possibilities are supported by auto-select.

Sources and Kinds are selected via checkbox. (The default state when none are checked is to show all.) As more of these items are selected, the records listing in the left-hand panel gets smaller. Also, the counts of available items [as shown by the (XX) number at the end of each item] are also changed as filters are added or subtracted by adding or removing checkboxes.

Applying filters to Attributes works a little differently. Attributes filters are selected by selecting the magnifier plus [] icon, which then brings up a filter selection at the top of the listing underneath the Attributes header.

The specific values and their counts (for the current selection population) is then shown; you may pick one or more items. Once done, you may pick another attribute to add to the filter list, and continue the filtering process.

Saving and Sharing Your Filters

sWebMaps have a useful way to save and share their active filter selections. At any point as you work with a sWebMap, you can save all of its current settings and configurations — viewport area, filter selections, and persistent records — via some simple steps.

You initiate this functionality by choosing the save button at the upper right of the map panel:

When that option is invoked, it brings up a dialog where you are able to name the current session, and provide whatever explanatory notes you think might be helpful.

NOTE: the naming and access to these saved sessions is local to your own use only, unless you choose to share the session with others; see below.

Once you have a saved session, you will then see a new control at the upper right of your map panel. This control is how you load any of your previously saved sessions:

Further, once you load a session, still further options are presented to you that enables you to either delete or share that session:

If you choose to share a session, a shortened URI is generated automatically for you:

If you then provide that URI link to another user, that user can then click on that link and see the map in the exact same state — viewport area, filter selections, and persistent records — as you initially saved. If the recipient then saves this session, it will now also be available persistently for his or her local use and changes.

NOTE: two users may interactively work together by sharing, saving and then modifying maps that they share again with their collaborator. [1] A semantic components is a JavaScript or Flex component or widget that takes record descriptions and irXML schema as input, and then outputs interactive visualizations of those records. Depending on the logic described in the input schema and the input record descriptions, the semantic component may behave differently or provide presentation options to users. Each semantic component delivers a very focused set of functionality or visualization. Multiple components may be combined on the same canvas for more complicated displays and controls. At present, there are 12 individual semantic widgets in the available open source suite; see further the sComponent category on the TechWiki. By convention, all of the individual widgets in the semantic component suite are named with an ‘s’ prefix; hence, sWebMap. [2] The open semantic framework, or OSF, is a combination of a layered architecture and an open-source, modular software stack. The stack combines many leading third-party software packages — such as Drupal for content management, Virtuoso for (RDF) triple storage, Solr for full-text indexing, GATE for tagging and natural language processing, the OWL2 API for ontology management and support, and others. These third-party tools are extended with open source developments from Structured Dynamics including structWSF (a RESTful Web services layer of about a dozen modules for interacting with the underlying data and data engines), conStruct (a series of Drupal modules that tie Drupal to the structWSF Web services layer), semantic components (data display and manipulation widgets, mostly based either in Flash or JavaScript, for working with the semantic data), various parsers and standard data exchange formats and schema to facilitate information flow amongst these options, and a ontologies layer, that consists of both domain ontologies that capture the coherent concepts and relationships of the current problem space and of administrative ontologies that govern how the other software layers interact with this structure. [3] structWSF is a platform-independent Web services framework for accessing and exposing structured RDF (Resource Description Framework) data. Its central organizing perspective is that of the dataset. These datasets contain instance records, with the structural relationships amongst the data and their attributes and concepts defined via ontologies (schema with accompanying vocabularies). The structWSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and open standards. The current structWSF framework has a baseline set of more than 20 Web services in CRUD, browse, search, tagging, ontology management, and export and import. [4] For the most comprehensive discussion of ODapps, see M. K. Bergman, 2011. ” Ontology-Driven Apps Using Generic Applications,” posted on the AI3:::Adaptive Information blog, March 7, 2011. You may also search on that blog for ‘ODapps‘ to see related content.

Shun the Frumious Bandersnatch?

The Web and open source have opened up a whole new world of opportunities and services. We can search the global information storehouse, connect with our friends and make new ones, form new communities, map where stuff is, and organize and display aspects of our lives and interests as never before. These advantages compound into still newer benefits via emergent properties such as social discovery or bookmarking, adding richness to our lives that heretofore had not existed.

And all of these benefits have come for free.

Of course, as our use and sophistication of the Web and open source have grown we have come to understand that the free provision of these services is rarely (ever?) unconditional. For search, our compact is to accept ads in return for results. For social networks, our compact is give up some privacy and control of our own identities. For open source, our compact is the acceptance of (generally) little or no support and often poor documentation.

We have come to understand this quid pro quo nature of free. Where the providers of these services tend to run into problems is when they change the terms of the compact. Google, for example, might change how its search results are determined or presented or how it displays its ads. Facebook might change its privacy or data capture policies. Or, OpenOffice or MySQL might be acquired by a new provider, Oracle, that changes existing distribution, support or community involvement procedures.

Sometimes changes may fit within the acceptable parameters of the compact. But, if such changes fundamentally alter the understood compact with the user community, users may howl or vote with their feet. Depending, the service provider may relent, the users may come to accept the new changes, or the user may indeed drop the service.

The Hidden Costs of Dependence

But there is another aspect of the use of free services, the implications of which have been largely unremarked. What happens if a service we have come to depend upon is no longer available?

Abandonment or changes in service may arise from bankruptcy or a firm being acquired by another. My favorite search service of a decade ago, AltaVista, and Delicious are two prominent examples here. Existing services may be dropped by a provider or APIs removed or deprecated. For Google alone, examples include Wave and Gears, Google Labs, and many, many APIs. (The howls around Google Translate actually caused it to be restored.) And existing services may be altered, such as moving from free to fee or having capabilities significantly modified. Ning and Babbel are two examples here. There are literally thousands of examples of Web-based free services that have gone through such changes. Most have not seen widespread use, but have affected their users nonetheless.

There is nothing unique about free services in these regards. Ford was able to cease production of its Edsel and change the form factor of the Thunderbird despite some loyal fans. Sugar Pops morphed into a variety of breakfast cereal brands. Sony Betamax was beat out by VHS, which then lost out to CDs and now DVDs. My beloved Saabs are heading for the dustbin, or Chinese ownership.

In all of these cases, as consumers we have no guarantees about the permanence of the service or the infrastructure surrounding it. The provider is solely able to make these determinations. It is no different when the service or offering is free. It is the reality of the marketplace that causes such changes.

But, somehow, with free Web services, it is easy to overlook these realities. I offer a couple of personal case studies.

Case Study #1: Site Search

I have earlier described the five different versions of site search that I have gone through for this blog. The thing is, my current option, Relevanssi, is also a free plug-in. What is notable about this example, though, is the multiple attempts and (unanticipated) significant effort to discover, evaluate and then implement alternatives. Unfortunately, I rather suspect my current option may itself — because of the nature of free on the Web — need to be replaced at some time down the road.

Case Study #2: FeedBurner

Part of what caused me to abandon Google Custom Search as one of the above search options was the requirement I serve ads on my blog to use it. So, when I decided to eliminate ads entirely in 2010 I not only gave up this search option, but I also lost some of the better tracking and analytics options also provided for free by Google. Fortunately, I had also adopted FeedBurner early in the life of this blog. It was also becoming increasingly clear that feed subscribers — in addition to direct site visitors — were becoming an essential metric for gauging traffic.

I thus had a replacement means for measuring traffic trends. Google (strange how it keeps showing up!) had purchased FeedBurner in 2007, and had made some nice site and feature improvements, including turning some paid services into free. The service was performing quite well, despite FeedBurner’s infamous knack to lose certain feed counts periodically. However, this performance broke last Summer when my site statistics indicated a massive drop in subscribers.

The figure below, courtesy of Feed Compare, shows the daily subscriber statistics for my AI3 blog for the past two years. The spikiness of the curve affirms the infamous statistics gaps of the service. The first part of the curve also shows nice, steady growth of readers, growing to more than 4000 by last Summer. Then, on August 16, there was a massive drop of 85% in my subscriber counts. I monitored this for a couple of days, thinking it was another temporary infamous event, then realized something more serious was afoot:

It was at this point I became active on the Google group for FeedBurner. Many others had noted the same service drop. (The major surmise is that FeedBurner now is having difficulty including Feedfetcher feeds, which is interesting because it is the feed of Google’s own Reader service, and the largest feed aggregation source on the Web.)

Over the ensuing months until last week I posted periodic notices to the official group seeking clarification as to the source of these errors and a fix to the service. In that period, no Google representative ever answered me, nor any of the numerous requests by others. I don’t believe there has been a single entry on any matter by Google staff for nearly the past year.

I made requests and inquiries no fewer than eight times over these months. True, Google had announced it was deprecating the FeedBurner API in May 2011, but, in that announcement, there was no indication that bug fixes or support to their own official group would cease. While it is completely within Google’s purview to do as it pleases, this behavior hardly lends itself to warm feelings by those using the service.

Finally, last week I dropped the FeedBurner stats and installed a replacement WordPress plugin service [1]. It was clear no fixes were forthcoming and I needed to regain an understanding of my actual subscriber base. The counts you now see on this site use this new service; they show the continuation of this site’s historical growth trend.

Is Google Becoming More Frumious?

It is not surprising that in the prior discussions Google figures prominently. It is the largest provider of APIs and free services on the Web. But, even with its continuing services, I am seeing trends that disturb me in terms of what I thought the “compact” was with the company.

I’m not liking recent changes to Google’s bread and butter, search. While they are doing much to incorporate more structure in their results, which I applaud, they are also making ranking, formatting and presentation changes I do not. I am now spending at least us much of my search time on DuckDuckGo, and have been mightily impressed with its cleanliness, quality and lack of ads in results.

I also do not like how all of my current service uses of Google are now being funneled into Google Plus. I am seeing an arrogance that Google knows what is best and wants to direct me to workflows and uses, reminiscent of the arrogance Microsoft came to assume at the height of its market share. How does that variant of Lord Acton’s dictum go? “Market share tends to corrupt, and absolute market share corrupts absolutely.”

We are seeing Google’s shift to monetize extremely popular APIs such as Maps and Translate. My company, Structured Dynamics, has utilized these services heavily for client work in the past. We now must find alternatives or cost the payment for these services into the ongoing economics of our customer installations. Of course, charging for these services is Google’s right, but it does change the equation and causes us to evaluate alternatives.

I fear that Google may be turning into a frumious Bandersnatch. I’m not sure we will shun it, but we certainly are changing our views of the basis by which we engage or not with the company and its services. Once we shift from a basis of free, our expectations as to permanence and support change as well.

Big Boys Don’t Cry

This is not a diatribe against Google nor a woe is us. Us big kids have come to know that there is no such thing as a free lunch. But that message is getting reaffirmed now more strongly in the Web context.

There can be benefits from seeking, installing or adapting to new alternatives with different service profiles when dependent services are abandoned or deprecated. Learning always takes place. Accepting one’s own responsibility for desired services also leads to control and tailoring for specific needs. Early use of free services also educates about what is desired or not, which can lead to better implementation choices if and when direct responsibility is assumed.

But, in some areas, we are seeing services or uses of the Web that we should adopt only with care or even shun. Business opportunities that depend on third-party services or APIs are very risky. Strong reliance on single-provider service ecosystems adds fragility to dependence. Own systems should be designed to not depend too strongly on specific API providers and their unique features or parameters.

Free is not forever, and it is conditional. Substitutability is a good design practice to embrace.

[1] I may detail at a later time how this replacement service was set up.

Coca-Cola, Toucans and Charles Sanders Peirce

The crowning achievement of the semantc Web is the simple use of URIs to identify data. Further, if the URI identifier can resolve to a representation of that data, it now becomes an integral part of the HTTP access protocol of the Web while providing a unique identifier for the data. These innovations provide the basis for distributed data at global scale, all accessible via Web devices such as browsers and smartphones that are now a ubiquitous part of our daily lives.

Yet, despite these profound and simple innovations, the semantic Web’s designers and early practitioners and advocates have been mired in a muddled, metaphysical argument of at least a decade over what these URIs mean, what they reference, and what their actual true identity is. These muddles about naming and identity, it might be argued, are due to computer scientists and programmers trying to grapple with issues more properly the domain of philosophers and linguists. But that would be unfair. For philosophers and linguists themselves have for centuries also grappled with these same conundrums [1].

As I argue in this piece, part of the muddle results from attempting to do too much with URIs while another part results from not doing enough. I am also not trying to directly enter the fray of current standards deliberations. (Despite a decade of controversy, I optimistically believe that the messy process of argument and consensus building will work itself out [2].) What I am trying to do in this piece, however, is to look to one of America’s pre-eminent philosophers and logicians, Charles Sanders Peirce (pronounced “purse”), to inform how these controversies of naming, identity and meaning may be dissected and resolved.

‘Identity Crisis’, httpRange-14, and Issue 57

The Web began as a way to hyperlink between documents, generally Web pages expressed in the HTML markup language. These initial links were called URLs (uniform resource locators), and each pointed to various kinds of electronic resources (documents) that could be accessed and retrieved on the Web. These resources could be documents written in HTML or other encodings (PDFs, other electronic formats), images, streaming media like audio or videos, and the like [3].

All was well and good until the idea of the semantic Web, which postulated that information about the real world — concepts, people and things — could also be referenced and made available for reasoning and discussion on the Web. With this idea, the scope of the Web was massively expanded from electronic resources that could be downloaded and accessed via the Web to now include virtually any topic of human discourse. The rub, of course, was that ideas such as abstract concepts or people or things could not be “dereferenced” nor downloaded from the Web.

One of the first things that needed to change was to define a broader concept of a URI “identifier” above the more limited concept of a URL “locator”, since many of these new things that could be referenced on the Web went beyond electronic resources that could be accessed and viewed [3]. But, since what the referent of the URI now actually might be became uncertain — was it a concept or a Web page that could be viewed or something else? — a number of commentators began to note this uncertainty as the “identity crisis” of the Web [4]. The topic took on much fervor and metaphysical argument, such that by 2003, Sandro Hawke, a staffer of the standards-setting W3C (World Wide Web Consortium), was able to say, “This is an old issue, and people are tired of it” [5].

Yet, for many of the reasons described more fully below, the issue refused to go away. The Technical Architecture Group (TAG) of the W3C took up the issue, under a rubric that came to be known as httpRange-14 [6]. The issue was first raised in March 2002 by Tim Berners-Lee, accepted for TAG deliberations in February 2003, with then a resolution offered in June 2005 [7]. (Refer to the original resolution and other information [6] to understand the nuances of this resolution, since particular commentary on that approach is not the focus of this article.) Suffice it to say here, however, that this resolution posited an entirely new distinction of Web content into “information resources” and “non-information resources”, and also recommended the use of the HTTP 303 redirect code for when agents requesting a URI should be directed to concepts versus viewable documents.

This “resolution” has been anything but. Not only can no one clearly distinguish these de novo classes of “information resources” [19], but the whole approach felt arbitrary and kludgy.

Meanwhile, the confusions caused by the “identity crisis” and httpRange-14 continued to perpetuate themselves. In 2006, a major workshop on “Identity, Reference and the Web” (IRW 2006) was held in conjunction with the Web’s major WWW2006 conference in Edinburgh, Scotland, on May 23, 2006 [8]. The various presentations and its summary (by Harry Halpin) are very useful to understand these issues. What was starting to jell at this time was the understanding that the basis of identity and meaning on the Web posed new questions, and ones that philosophers, logicians and linguists needed to be consulted to help inform.

The fiat of the TAG’s 2005 resolution has failed to take hold. Over the ensuing years, various eruptions have occurred on mailing lists and within the TAG itself (now expressed as Issue 57) to revisit these questions and bring the steps moving forward into some coherent new understanding. Though linked data has been premised on best-practice implementation of these resolutions [9], and has been a qualified success, many (myself included) would claim that the extra steps and inefficiencies required from the TAG’s httpRange-14 guidance have been hindrances, not facilitators, of the uptake of linked data (or the semantic Web).

Today, despite the efforts of some to claim the issue closed, it is not. Issue 57 and the periodic bursts from notable semantic Web advocates such as Ian Davis [10], Pat Hayes and Harry Halpin [11], Ed Summers [12], Xiaoshu Wang [13], David Booth [14] and TAG members themselves, such as Larry Masinter [15] and Jonathan Rees [16], point to continued irresolution and discontent within the advocate community. Issue 57 currently remains open. Meanwhile, I think, all of us interested in such matters can express concern that linked data, the semantic Web and interoperable structured data have seen less uptake than any of us had hoped or wanted over the past decade. As I have stated elsewhere, unclear semantics and muddled guidelines help to undercut potential use.

As each of the eruptions over these identity issues has occurred, the competing camps have often been characterized as “talking past one another”; that is, not communicating in such a way as to help resolve to consensus. While it is hardly my position to do so, I try to encapsulate below the various positions and prejudices as I see them in this decades-long debate. I also try to share my own learning that may help inform some common ground. Forgive me if I overly simplify these vexing issues by returning to what I see as some first principles . . . .

What’s in a Name?

One legacy of the initial document Web is the perception that Web addresses have meaning. We have all heard of the multi-million dollar purchasing of domains [17] and the adjudication that may occur when domains are hijacked from their known brands or trademark owners. This legacy has tended to imbue URIs with a perceived value. It is not by accident, I believe, that many within the semantic Web and linked data communities still refer to “minting” URIs. Some believe that ownership and control over URIs may be equivalent to grabbing up valuable real estate. It is also the case that many believe the “name” given to a URI acts to name the referent to which it refers.

This perception is partially true, partially false, but moreover incomplete in all cases. We can illustrate these points with the global icon, “Coca-Cola”.

As for the naming aspects, let’s dissect what we mean when we use the label “Coca-Cola” (in a URI or otherwise). Perhaps the first thing that comes to mind is “Coca-Cola,” the beverage (which has a description on Wikipedia, among other references). Because of its ubiquity, we may also recognize the image of the Coca-Cola bottle to the left as a symbol for this same beverage. (Though, in the hilarious movie, The Gods, They Must be Crazy, Kalahari Bushmen, who had no prior experience of Coca-Cola, took the bottle to be magical with evil powers [18].) Yet even as reference to the beverage, the naming aspects are a bit cloudy since we could also use the fully qualified synonyms of “Coke”, “Coca-cola” (small C), “Classic Coke” and the hundreds of language variants worldwide.

On the other hand, the label “Coca-Cola” could just as easily conjure The Coca-Cola Company itself. Indeed, the company web site is the location pointed to by the URI of http://www.thecoca-colacompany.com/. But, even that URI, which points to the home Web page of the company, does not do justice to conveying an understanding or description of the company. For that, additional URIs may need to be invoked, such as the description at Wikipedia, the company’s own company description page, plus perhaps the company’s similar heritage page.

Of course, even these links and references only begin to scratch the surface of what the company Coca-Cola actually is: headquarters, manufacturing facilities, 140,000 employees, shareholders, management, legal entities, patents and Coke recipe, and the like. Whether in human languages or URIs, in any attempt to signify something via symbols or words (themselves another form of symbol), we risk ambiguity and incompleteness.

URI shorteners also undercut the idea that a URI necessarily “names” something. Using the service bitly, we can shorten the link to the Wikipedia description of the Coke beverage to http://bit.ly/xnbA6 and we can shorten the link to The Coca-Cola Company Web site to http://bit.ly/9ojUpL. I think we can fairly say that neither of these shortened links “name” their referents. The most we can say about a URI is that it points to something. With the vagaries of meaning in human languages, we might also say that URIs refer to something, denote something or identify (but not in the sense of completely define) something.

From this discussion, we can assert with respect to the use of URIs as “names” that:

1. In all cases, URIs are pointers to a particular referent
2. In some cases, URIs do act to “name” some things
3. Yet, even when used as “names,” there can be ambiguity as to what exactly the referent is that is denoted by the name
4. Resolving what such “names” mean is a matter of context and reference to further information or links, and
5. Because URIs may act as “names”, it is appropriate to consider social conventions and contracts (e.g., trademarks, brands, legal status) in adjudicating who can own the URI.

In summary, I think we can say that URIs may act as names, but not in all or most cases, and when used as such are often ambiguous. Absolutely associating URIs as names is way too heavy a burden, and incorrect in most cases.

What is a Resource?

The “name” discussion above masks that in some cases we are talking about a readable Web document or image (such as the Wikipedia description of the Coke beverage or its image) versus the “actual” thing in the real world (the Coke beverage itself or even the company). This distinction is what led to the so-called “identity crisis”, for which Ian Davis has used a toucan as his illustrative thing [10].

As I note in the conclusion, I like Davis’ approach to the identity conundrum insofar as Web architecture and linked data guidance are concerned. But here my purpose is more subtle: I want to tease apart still further the apparent distinction between an electronic description of something on the Web and the “actual” something. Like Davis, let’s use the toucan.

In our strawman case, we too use a description of the toucan (on Wikipedia) to represent our “information resource” (the accessible, downloadable electronic document). We contrast to that a URI that we mean to convey the actual physical bird (a “non-information resource” in the jumbled jargon of httpRange-14), which we will designate via the URI of http://example.com/toucan.

Despite the tortured (and newly conjured) distinction between “information resource” and “non-information resource”, the first blush reaction is that, sure, there is a difference between an electronic representation that can be accessed and viewed on the Web and its true, “actual” thing. Of course people can not actually be rendered and downloaded on the Web, but their bios and descriptions and portrait images may. While in the abstract such distinctions appear true and obvious, in the specifics that get presented to experts, there is surprising disagreement as to what is actually an “information resource” v. a “non-information resource” [19]. Moreover, as we inspect the real toucan further, even that distinction is quite ambiguous.

When we inspect what might be a definitive description of “toucan” on Wikipedia, we see that the term more broadly represents the family of Ramphastidae, which contains five genera and forty different species. The picture we are showing to the right is but of one of those forty species, that of the keel-billed toucan (Ramphastos sulfuratus). Viewing the images of the full list of toucan species shows just how divergent these various “physical birds” are from one another. Across all species, average sizes vary by more than a factor of three with great variation in bill sizes, coloration and range. Further, if I assert that the picture to the right is actually that of my pet keel-billed toucan, Pretty Bird, then we can also understand that this representation is for a specific individual bird, and not the physical keel-billed toucan species as a whole.

The point of this diversion is not a lecture on toucans, but an affirmation that distinctions between “resources” occur at multiple levels and dimensions. Just as there is no self-evident criteria as to what constitutes an “information resource”, there is also not a self-evident and fully defining set of criteria as to what is the physical “toucan” bird. The meaning of what we call a “toucan” bird is not embodied in its label or even its name, but in the context and accompanying referential information that place the given referent into a context that can be communicated and understood. A URI points to (“refers to”) something that causes us to conjure up an understanding of that thing, be it a general description of a toucan, a picture of a toucan, an understanding of a species of toucan, or a specific toucan bird. Our understanding or interpretation results from the context and surrounding information accompanying the reference.

In other words, a “resource” may be anything, which is just the way the W3C has defined it. There is not a single dimension which, magically, like “information” and “non-information,” can cleanly and definitely place a referent into some state of absolute understanding. To assert that such magic distinctions exist is a flaw of Cartesian logic, which can only be reconciled by looking to more defensible bases in logic [20].

Peirce and the Logic of Signs

The logic behind these distinctions and nuances leads us to Charles Sanders Peirce (1839 – 1914). Peirce (pronounced “purse”) was an American logician, philosopher and polymath of the first rank. Along with Frege, he is acknowledged as the father of predicate calculus and the notation system that formed the basis of first-order logic. His symbology and approach arguably provide the logical basis for description logics and other aspects underlying the semantic Web building blocks of the RDF data model and, eventually, the OWL language. Peirce is the acknowledged founder of pragmatism, the philosophy of linking practice and theory in a process akin to the scientific method. He was also the first formulator of existential graphs, an essential basis to the whole field now known as model theory. Though often overlooked in the 20th century, Peirce has lately been enjoying a renaissance with his voluminous writings still being deciphered and published.

The core of Peirce’s world view is based in semiotics, the study and logic of signs. In his seminal writing on this, “What is in a Sign?” [21], he wrote that “every intellectual operation involves a triad of symbols” and “all reasoning is an interpretation of signs of some kind”. Peirce had a predilection for expressing his ideas in “threes” throughout his writings.

Semiotics is often split into three branches: 1) syntactics – relations among signs in formal structures; 2) semantics – relations between signs and the things to which they refer; and 3) pragmatics – relations between signs and the effects they have on the people or agents who use them.

Peirce’s logic of signs in fact is a taxonomy of sign relations, in which signs get reified and expanded via still further signs, ultimately leading to communication, understanding and an approximation of “canonical” truth. Peirce saw the scientific method as itself an example of this process.

A given sign is a representation amongst the triad of the sign itself (which Peirce called a representamen, the actual signifying item that stands in a well-defined kind of relation to the two other things), its object and its interpretant. The object is the actual thing itself. The interpretant is how the agent or the perceiver of the sign understands and interprets the sign. Depending on the context and use, a sign (or representamen) may be either an icon (a likeness), an indicator or index (a pointer or physical linkage to the object) or a symbol (understood convention that represents the object, such as a word or other meaningful signifier).

An interpretant in its barest form is a sign’s meaning, implication, or ramification. For a sign to be effective, it must represent an object in such a way that it is understood and used again. This makes the assignment and use of signs a community process of understanding and acceptance [20], as well as a truth-verifying exercise of testing and confirming accepted associations.

John Sowa has done much to help make some of Peirce’s obscure language and terminology more accessible to lay readers [22]. He has expressed Peirce’s basic triad of sign relations as follows, based around the Yojo animist cat figure used by the character Queequeg in Herman Melville’s Moby-Dick:

In this figure, object and symbol are the same as the Peirce triad; concept is the interpretant in this case. The use of the word ‘Yojo’ conjures the concept of cat.

This basic triad representation has been used in many contexts, with various replacements or terms at the nodes. Its basic form is known as the Meaning Triangle, as was popularized by Ogden and Richards in 1923 [23].

The key aspect of signs for Peirce, though, is the ongoing process of interpretation and reference to further signs, a process he called semiosis. A sign of an object leads to interpretants, which, as signs, then lead to further interpretants. In the Sowa example below, we show how meaning triangles can be linked to one another, in this case by abstracting that the triangles themselves are concepts of representation; we can abstract the ideas of both concept and symbol:

We can apply this same cascade of interpretation to the idea of the sign (or representamen), which in this case shows that a name can be related to a word symbol, which in itself is a combination of characters in a string called ‘Yojo’:

According to Sowa [22]:

“What is revolutionary about Peirce’s logic is the explicit recognition of multiple universes of discourse, contexts for enclosing statements about them, and metalanguage for talking about the contexts, how they relate to one another, and how they relate to the world and all its events, states, and inhabitants. “The advantage of Peircean semiotics is that it firmly situates language and logic within the broader study of signs of all types. The highly disciplined patterns of mathematics and logic, important as they may be for science, lie on a continuum with the looser patterns of everyday speech and with the perceptual and motor patterns, which are organized on geometrical principles that are very different from the syntactic patterns of language or logic.”

Catherine Legg [20] notes that the semiotic process is really one of community involvement and consensus. Each understanding of a sign and each subsequent interpretation helps come to a consensus of what a sign means. It is a way of building a shared understanding that aids communication and effective interpretation. In Peirce’s own writings, the process of interpretation can lead to validation and an eventual “canonical” or normative interpretation. The scientific method itself is an extreme form of the semiotic process, leading ultimately to what might be called accepted “truths”.

Peircean Semiotics of URIs

So, how do Peircean semiotics help inform us about the role and use of URIs? Does this logic help provide guidance on the “identity crisis”?

The Peircean taxonomy of signs has three levels with three possible sign roles at each level, leading to a possible 27 combinations of sign representations. However, because not all sign roles are applicable at all levels, Peirce actually postulated only ten distinct sign representations.

Common to all roles, the URI “sign” is best seen as an index: the URI is a pointer to a representation of some form, be it electronic or otherwise. This representation bears a relation to the actual thing that this referent represents, as is true for all triadic sign relationships. However, in some contexts, again in keeping with additional signs interpreting signs in other roles, the URI “sign” may also play the role of a symbolic “name” or even as a signal that the resource can be downloaded or accessed in electronic form. In other words, by virtue of the conventions that we choose to assign to our signs, we can supply additional information that augments our understanding of what the URI is, what it means, and how it is accessed.

Of course, in these regards, a URI is no different than any other sign in the Peircean world view: it must reside in a triadic relationship to its actual object and an interpretation of that object, with further understanding only coming about by the addition of further signs and interpretations.

In shortened form, this means that a URI, acting alone, can at most play the role of a pointer between an object and its referent. A URI alone, without further signs (information), can not inform us well about names or even what type of resource may be at hand. For these interpretations to be reliable, more information must be layered on, either by accepted convention of the current signs or the addition of still further signs and their interpretations. Since the attempts to deal with the nature of a URI resource by fiat as stipulated by httpRange-14 neither meet the standards of consensus nor empirical validity, the attempt can not by definition become “canonical”. This does not mean that httpRange-14 and its recommended practices can not help in providing more information and aiding interpretation for what the nature of a resource may be. But it does mean that httpRange-14 acting alone is insufficient to resolve ambiguity.

Moreover, what we see in the general nature of Peirce’s logic of signs is the usefulness of adding more “triads” of representation as the process to increase understanding through further interpretation. Kind of sounds like adding on more RDF triples, does it not?

Global is Neither Indiscriminate Nor Unambiguous

Names, references, identity and meaning are not absolutes. They are not philosophically, and they are not in human language. To expect machine communications to hold to different standards and laws than human communications is naive. To effect machine communications our challenge is not to devise new rules, but to observe and apply the best rules and practices that human communications instruct.

There has been an unstated hope at the heart of the semantic Web enterprise that simply expressing statements in the right way (syntax) and in the right form (RDF) is sufficient to facilitate machine communications. But this hope, too, is naive and silly. Just as we do not accept all human utterances as truth, neither will we accept all machine transmissions as reliable. Some of the information will be posted in error; some will be wrong or ill-fitting to our world view; some will be malicious or intended to deceive. Spam and occasionally lousy search results on the Web tell us that Web documents are subject to these sources of unsuitability, why is not the same true of data?

Thus, global data access via the semantic Web is not — and can never be — indiscriminate nor unambiguous. We need to understand and come to trust sources and provenance; we need interpretation and context to decide appropriateness and validity; and we need testing and validation to ensure messages as received are indeed correct. Humans need to do these things in their normal courses of interaction and communication; our machine systems will need to do the same.

These confirmations and decisions as to whether the information we receive is actionable or not will come about via still more information. Some of this information may come about via shared conventions. But most will come about because we choose to provide more context and interpretation for the core messages we hope to communicate.

A Go-Forward Approach

Nearly five years ago Hayes and Halpin put forth a proposal to add ex:refersTo and ex:describedBy to the standard RDF vocabulary as a way for authors to provide context and explanation for what constituted a specific RDF resource [11]. In various ways, many of the other individuals cited in this article have come to similar conclusions. The simple redirect suggestions of both Ian Davis [10] and Ed Summers [12] appear particularly helpful.

Over time, we will likely need further representations about resources regarding such things as source, provenance, context and other interpretations that would help remove ambiguities as to how the information provided by that resource should be consumed or used. These additional interpretations can mechanically be provided via referenced ontologies or embedded RDFa (or similar). These additional interpretations can also be aided by judicious, limited additions of new predicates to basic language specifications for RDF (such as the Hayes and Halpin suggestions).

In the end, of course, any frameworks that achieve consensus and become widely adopted will be simple to use, easy to understand, and straightforward to deploy. The beauty of best practices in predicates and annotations is that failures to provide are easy to test. Parties that wish to have their data consumed have incentive to provide sufficient information so as to enable interpretation.

There is absolutely no reason that these additions can not co-exist with the current httpRange-14 approach. By adding a few other options and making clear the optional use of httpRange-14, we would be very Peirce-like in our go-forward approach: We are being both pragmatic while we add more means to improve our interpretations for what a Web resource is and is meant to be.

[1] Throughout intellectual history, a number of prominent philosophers and logicians have attempted to describe naming, identity and reference of objects and entities. Here are a few that you may likely encounter in various discussions of these topics in reference to the semantic Web; many are noted philosophers of language:

• Aristotle (384 BC – 322 BC) – founder of formal logic; formulator and proponent of categorization; believed in the innate “universals” of various things in the natural world
• Rudolf Carnap (1891 – 1970) -  proposed a logical syntax that provided a system of concepts, a language, to enable logical analysis via exactly formula; a basis for natural language processing;rejected the idea and use of metaphysics
• René Descartes (1596 – 1650) – posited a boundary between mind and the world; the meaning of a sign is the intension of its producer, and is private and incorrigible
• Friedrich Ludwig Gottlob Frege (1848 – 1925) – one of the formulators of first-order logic, though syntax not adopted; advocated shared senses, which can be objective and sharable
• Kurt Gödel (1906 – 1978) – his two incompleteness theorems are some of the most important logic contributions of all time; they establish inherent limitations of all but the most trivial axiomatic systems capable of doing arithmetic, as well as for computer programs
  
• David Hume (1711 – 1776) – embraced natural empiricism, but kept the Descartes concept of an “idea”
• Immanuel Kant (1724 – 1804) – one of the major philosophers in history, argued that experience is purely subjective without first being processed by pure reason; a major influence on Peirce
• Saul Kripke (1940 – ) – proposed the causal theory of reference and what proper names mean via a “baptism” by the namer
• Gottfried Wilhelm Leibniz (1646 – 1716) – the classic definition of identity is Leibniz’s Law, which states that if two objects have all of their properties in common, they are identical and so only one object
• Richard Montague (1930 – 1971) – wrote much on logic and set theory; student of Tarski; pioneered a logical approach to natural language semantics; associated with model theory, model-theoretic semantics
• Charles Sanders Peirce (1839 – 1914) – see main text
• Willard Van Orman Quine (1908 – 2000) – noted analytical philosopher, advocated the “radical indeterminancy of translation” (can never really know)
• Bertrand Russell (1872 – 1970) – proposed the direct theory of reference and what it means to “ground in references”; adopted many Peirce arguments without attribution
• Ferdinand de Saussure (1857 – 1913) – also proposed an alternative view to Peirce of semiotics, one grounded in sociology and linguistics
• John Rogers Searle (1932 – ) – argues that consciousness is a real physical process in the brain and is subjective; has argued against strong AI (artificial intelligence)
• Alfred Tarski (1901 – 1983) – analytic philosopher focused on definitions of models and truth; great admirer of Peirce; associated with model theory, model-theoretic semantics
• Ludwig Josef Johann Wittgenstein (1889 – 1951) – he disavowed his earlier work, arguing that philosophy needed to be grounded in ordinary language, recognzing that the meaning of words is dependent on context, usage, and grammar.

Also, Umberto Eco has been a noted proponent and popularizer of semiotics. [2] As any practitioner ultimately notes, standards development is a messy, lengthy and trying process. Not all individuals can handle the messiness and polemics involved. Personally, I prefer to try to write cogent articles on specific issues of interest, and then leave it to others to slug it out in the back rooms of standards making. Where the process works well, standards get created that are accepted and adopted. Where the process does not work well, the standards are not embraced as exhibited by real-world use. [3] Tim Berners-Lee, 2007. What Do HTTP URIs Identify? This article does not discuss the other sub-category of URIs, URNs (for names). URNs may refer to any standard naming scheme (such as ISBNs for books) and has no direct bearing on any network access protocol, as do URLs and URIs when they are referenceable. Further, URNs are little used in practice. [4] Kendall Clark was one of the first to question “resource” and other identity ambiguities, noting the tautology between URI and resource as “anything that has identity.” See Kendall Clark, 2002. “Identity Crisis,” in XML.com, Sept 11 2002; see http://www.xml.com/pub/a/2002/09/11/deviant.html. From the topic map community, one notable contribution was from Steve Pepper and Sylvia Schwab, 2003. “Curing the Web’s Identity Crisis,” found at : http://www.ontopia.net/topicmaps/materials/identitycrisis.html. [5] Sandro Hawke, 2003. Disambiguating RDF Identifiers. W3C, January 2003. See http://www.w3.org/2002/12/rdf-identifiers/. [6] The issue was framed as what is the proper “range” for HTTP referrals and was also the 14th major TAG issue recorded, hence the name. See further the httpRange-14 Webography . [7] See W3C, “httpRange-14: What is the range of the HTTP dereference function?”; see http://www.w3.org/2001/tag/issues.html#httpRange-14. [8] See http://www.ibiblio.org/hhalpin/irw2006/. [9] Leo Sauermann and Richard Cyganiak, eds., 2008. Cool URIs for the Semantic Web, W3C Interest Group Note, December 3, 2008. See http://www.w3.org/TR/cooluris/. [10] Ian Davis, 2010. Is 303 Really Necessary? Blog post, November 2010, accessed 20 January 2012. (See http://blog.iandavis.com/2010/11/04/is-303-really-necessary/.) A considerable thread resulted from this post; see http://markmail.org/thread/mkoc5kxll6bbjbxk. [11] See first Harry Halpin, 2006. “Identity, Reference and Meaning on the Web,” presented at WWW 2006, May 23, 2006. See http://www.ibiblio.org/hhalpin/irw2006/hhalpin.pdf. This was then followed up with greater elaboration by Patrick J. Hayes and Harry Halpin, 2007. “In Defense of Amibiguity,” http://www.ibiblio.org/hhalpin/homepage/publications/indefenseofambiguity.html. [12] Ed Summers, 2010. Linking Things and Common Sense, blog post of July 7, 2010. See http://inkdroid.org/journal/2010/07/07/linking-things-and-common-sense/. [13] Xiaoshu Wang, 2007. URI Identity and Web Architecture Revisited, Word document posted on posterous.com, November 2007. (Former Web documents have been removed.) [14] David Booth, 2006. “URIs and the Myth of Resource Identity,” see http://dbooth.org/2006/identity/. [15] See Larry Masinter, 2012. “The ‘tdb’ and ‘duri’ URI Schemes, Based on Dated URIs,” 10th version, IETF Network Working Group Internet-Draft,January 12, 2012. See http://tools.ietf.org/html/draft-masinter-dated-uri-10. [16] Jonathan Rees has been the scribe and author for many of the background documents related to Issue 57. A recent mailing list entry provides pointers to four relevant documents in this entire discussion. See Jonathan A Rees, 2012. Guide to ISSUE-57 (httpRange-14) document suiteJanuary, 21, 2012. [17] At least twenty domain names, led by insure.com, have sold for more the $2 million each; see this Wikipedia listing. [18] In the wonderful movie, The Gods, They Must be Crazy, Bushmen in the Kalahari Desert one day find an unbroken glass Coke bottle that had been thrown out of an airplane. Initially, this strange artifact seems to be another boon from the gods, and the Bushmen find many uses for it. But unlike anything that they have had before, there is only one bottle to go around. This creates jealousy, envy, anger, hatred, even violence. The protagonist, Xi, decides that the bottle is an evil thing and must be thrown off of the edge of the world. The hilarity of the movie comes from that premise and Xi’s encounters with the modern world as he pursues his quest with the magic bottle. [19] Wang [13]rhetorically asked which of the following things would be categorized as an “information resource”:

1. A book
2. A clock
3. The clock on the wall of my bedroom
4. A gene
5. The sequence of a gene
6. A software
7. A service
8. A namespace
9. An ontology
10. A language
11. A number
12. A concept, such as Dublin Core’s creator.

See the 2007 thread on this issue, mostly by Sean Palmer and Noah Mendelsohn, the latter aknowledging that various experts may only agree on 85% of the items.

[20] See further Catherine Legg, 2010. “Pragmaticsm on the Semantic Web,” in Bergman, M., Paavola, S., Pietarinen, A.-V., & Rydenfelt, H. eds., Ideas in Action: Proceedings of the Applying Peirce Conference, pp. 173–188. Nordic Studies in Pragmatism 1. Helsinki: Nordic Pragmatism Network. See http://www.nordprag.org/nsp/1/Legg.pdf. [21] Charles Sanders Peirce, 1894. “What is in a Sign?”, see http://www.iupui.edu/~peirce/ep/ep2/ep2book/ch02/ep2ch2.htm. [22] The figures in particular are from John F. Sowa, 2000. “Ontology, Metadata, and Semiotics,” presented at ICCS 2000 in Darmstadt, Germany, on August 14, 2000; published in B. Ganter & G. W. Mineau, eds., Conceptual Structures: Logical, Linguistic, and Computational Issues, Lecture Notes in AI #1867, Springer-Verlag, Berlin, 2000, pp. 55-81. May be found at http://www.jfsowa.com/ontology/ontometa.htm. Also see John F. Sowa, 2006. “Peirce’s Contributions to the 21st Century,” presented at International Conference on Conceptual Structures, Aalborg, Denmark, July 17, 2006. See http://www.jfsowa.com/pubs/csp21st.pdf. [23] C.K. Ogden and I. A. Richards, 1923. The Meaning of Meaning, Harcourt, Brace, and World, New York, 8th edition 1946.