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Why Clojure?

AI3:::Adaptive Information (Mike Bergman) - Wed, 12/10/2014 - 16:40
Ten Management Reasons for Choosing Clojure for Adaptive Knowledge Apps

It is not unusual to see articles touting one programming language or listing the reasons for choosing another, but they are nearly always written from the perspective of the professional developer. As an executive with much experience directing software projects, I thought a management perspective could be a useful addition to the dialog. My particular perspective is software development in support of knowledge management, specifically leveraging artificial intelligence, semantic technologies, and data integration.

Context is important in guiding the selection of programming languages. C is sometimes a choice for performance reasons, such as in data indexing or transaction systems. Java is the predominant language for enterprise applications and enterprise-level systems. Scripting languages are useful for data migrations and translations and quick applications. Web-based languages of many flavors help in user interface development or data exchange. Every one of the hundreds of available programming languages has a context and rationale that is argued by advocates.

We at Structured Dynamics have recently made a corporate decision to emphasize the Clojure language in the specific context of knowledge management development. I’d like to offer our executive-level views for why this choice makes sense to us. Look to the writings of SD’s CTO, Fred Giasson, for arguments related to the perspective of the professional developer.

Some Basic Management Premises

I have overseen major transitions in programming languages from Fortran or Cobol to C, from C to C++ and then Java, and from Java to more Web-oriented approaches such as Flash, JavaScript and PHP [1]. In none of these cases, of course, was a sole language adopted by the company, but there always was a core language choice that drove hiring and development standards. Making these transitions is never easy, since it affects employee choices, business objectives and developer happiness and productivity. Because of these trade-offs, there is rarely a truly “correct” choice.

Languages wax and wane in popularity, and market expectations and requirements shift over time. Twenty years ago, Java brought forward a platform-independent design well-suited for client-server computing, and was (comparatively) quickly adopted by enterprises. At about the same time Web developments added browser scripting languages to the mix. Meanwhile, hardware improvements overcame many previous performance limitations in favor of easier to use syntaxes and tooling. No one hardly programs for an assembler anymore. Sometimes, like Flash, circumstances and competition may lead to a rapid (and unanticipated) abandonment.

The fact that such transitions naturally occur over time, and the fact that distributed and layered architectures are here to stay, has led to my design premise to emphasize modularity, interfaces and APIs [2]. From the browser, client and server sides we see differential timings of changes and options. It is important that piece parts be able to be swapped out in favor of better applications and alternatives. Irrespective of language, architectural design holds the trump card in adaptive IT systems.

Open source has also had a profound influence on these trends. Commercial and product offerings are no longer monolithic and proprietary. Rather, modern product development is often more based on assembly of a diversity of open source applications and libraries, likely written in a multitude of languages, which are then assembled and “glued” together, often with a heavy reliance on scripting languages. This approach has certainly been the case with Structured Dynamics’ Open Semantic Framework, but OSF is only one example of this current trend.

The trend to interoperating open source applications has also raised the importance of data interoperability (or ETL in various guises) plus reconciling semantic heterogeneities in the underlying schema and data definitions of the contributing sources. Language choices increasingly must recognize these heterogeneities.

I have believed strongly in the importance of interest and excitement by the professional developers in the choice of programming languages. The code writers — be it for scripting or integration or fundamental app development — know the problems at hand and read about trends and developments in programming languages. The developers I have worked with have always been the source of identifying new programming options and languages. Professional developers read much and keep current. The best programmers are always trying and testing new languages.

I believe it is important for management within software businesses to communicate anticipated product changes within the business to its developers. I believe it is important for management to signal openness and interest in hearing the views of its professional developers in language trends and options. No viable software development company can avoid new upgrades of its products, and choices as to architecture and language must always be at the forefront of version planning.

When developer interest and broader external trends conjoin, it is time to do serious due diligence about a possible change in programming language. Tooling is important, but not dispositive. Tooling rapidly catches up with trending and popular new languages. As important to tooling is the “fit” of the programming language to the business context and the excitement and productivity of the developers to achieve that fit.

Fitness” is a measure of adaptiveness to a changing environment. Though I suppose some of this can be quantified — as it can in evolutionary biology — I also see “fit” as a qualitative, even aesthetic, thing. I recall sensing the importance of platform independence and modularity in Java when it first came out, results (along with tooling) that were soon borne out. Sometimes there is just a sense of “rightness” or alignment when contemplating a new programming language for an emerging context.

Such is the case for us at Structured Dynamics with our recent choice to emphasize Clojure as our core development language. This choice does not mean we are abandoning our current code base, just that our new developments will emphasize Clojure. Again, because of our architectural designs and use of APIs, we can make these transitions seamlessly as we move forward.

However, for this transition, unlike prior ones I have made noted above, I wanted to be explicit as to the reasons and justifications. Presenting these reasons for Clojure is the purpose of this article.

Brief Overview of Clojure

Clojure is a relatively new language, first released in 2007 [3]. Clojure is a dialect of Lisp, explicitly designed to address some Lisp weaknesses in a more modern package suitable to the Web and current, practical needs. Clojure is a functional programming language, which means it has roots in the lamdba calculus and functions are “first-class citizens” in that they can be passed as arguments to other functions, returned as values, or assigned as variables in data structures. These features make the language well suited to mathematical manipulations and the building up of more complicated functions from simpler ones.

Clojure was designed to run on the Java Virtual Machine (JVM) (now expanded to other environments such as ClojureScript and Clojure CLR) rather than any specific operating system. It was designed to support concurrency. Other modern features were added in relation to Web use and scalability. Some of these features are elaborated in the rationales noted below.

As we look to the management reasons for selecting Clojure, we can really lump them into two categories: a) those that arise mostly from Lisp, as the overall language basis; and b) specific aspects added to Clojure that overcome or enhance the basis of Lisp.

Reasons Deriving from Lisp

Lisp (defined as a list processing language) is one of the older computer languages around, dating back to 1958, and has evolved to become a family of languages. “Lisp” has many variants, with Common Lisp one of the most prevalent, and many dialects that have extended and evolved from it.

Lisp was invented as a language for expressing algorithms. Lisp has a very simple syntax and (in base form) comparatively few commands. Lisp syntax is notable (and sometimes criticized) for its common use of parentheses, in a nested manner, to specify the order of operations.

Lisp is often associated with artificial intelligence programming, since it was first specified by John McCarthy, the acknowledged father of AI, and was the favored language for many early AI applications. Many have questioned whether Lisp has any special usefulness to AI or not. Though it is hard to point to any specific reason why Lisp would be particularly suited to artificial intelligence, it does embody many aspects highly useful to knowledge management applications. It was these reasons that caused us to first look at Lisp and its variants when we were contemplating language alternatives:

  1. Open — I have written often that knowledge management, given its nature grounded in the discovery of new facts and connections, is well suited to being treated under the open world assumption [4]. OWA is a logic that explicitly allows the addition of new assertions and connections, built upon a core of existing knowledge. In both optical and literal senses, the Lisp language is an open one that is consistent with OWA. The basis of Lisp has a “feel” much like RDF (Resource Description Framework), the prime language of OWA. RDF is built from simple statements, as is Lisp. New objects (data) can be added easily to both systems. Adding new predicates (functions) is similarly straightforward. Lisp’s nested parentheses also recall Boolean logic, another simple means for logically combining relationships. As with semantic technologies, Lisp just seems “right” as a framework for addressing knowledge problems;
  2. Extensible — one of the manifestations of Lisp’s openness is its extensiblity via macros. Macros are small specifications that enable new functions to be written. This extensibility means that a “bottom up” programming style can be employed [5] that allows the syntax to be expanded with new language to suit the problem, leading in more complete cases to entirely new — and tailored to the problem — domain-specific languages (DSLs). As an expression of Lisp’s openness, this extensibility means that the language itself can morph and grow to address the knowledge problems at hand. And, as that knowledge continues to grow, as it will, so may the language by which to model and evaluate it;
  3. Efficient — Lisp, then, by definition, has a minimum of extraneous code functions and, after an (often steep) initial learning curve, rapid development of appropriate ones for the domain. Since anything Lisp can do to a data structure it can do to code, it is an efficient language. When developing code, Lisp provides a read-eval-print-loop (REPL) environment that allows developers to enter single expressions (s-expressions) and evaluate them at the command line, leading to faster and more efficient ways to add features, fix bugs, and test code. Accessing and managing data is similarly efficient, along with code and data maintenance. A related efficiency with Lisp is lazy evaluation, wherein only the given code or data expression at hand is evaluated as its values are needed, rather than building evaluation structures in advance of execution;
  4. Data-centric — the design aspect of Lisp that makes many of these advantages possible is its data-centric nature. This nature comes from Lisp’s grounding in the lambda calculus and its representation of all code objects via an abstract syntax tree. These aspects allow data to be immutable, and for data to act as code, and code to act as data [6]. Thus, while we can name and reference data or functions as in any other language, they are both manipulated and usable in the same way. Since knowledge management is the combination of schema with instance data, Lisp (or other homoiconic languages) is perfectly suited; and,
  5. Malleable — a criticism of Lisp through the decades has been its proliferation of dialects, and lack of consistency between them. This is true, and Lisp is therefore not likely the best vehicle in its native form for interoperability. (It may also not be the best basis for large-scale, complicated applications with responsive user interfaces.) But for all of the reasons above, Lisp can be morphed into many forms, including the manipulation of syntax. In such malleable states, the dialect maintains its underlying Lisp advantages, but can be differently purposed for different uses. Such is the case with Clojure, discussed in the next section.
Reasons Specific to the Clojure Language

Clojure was invented by Rich Hickey, who knew explicitly what he wanted to accomplish leveraging Lisp for new, more contemporary uses [7]. (Though some in the Lisp community have bristled against the idea that dialects such as Common Lisp are not modern, the points below really make a different case.) Some of the design choices behind Clojure are unique and quite different from the Lisp legacy; others leverage and extend basic Lisp strengths. Thus, with Clojure, we can see both a better Lisp, at least for our stated context, and one designed for contemporary environments and circumstances.

Here are what I see as the unique advantages of Clojure, again in specific reference to the knowledge management context:

  1. Virtual machine design (interoperability) — the masterstroke in Clojure is to base it upon the Java Virtual Machine. All of Lisp’s base functions were written to use the JVM. Three significant advantages accrue from this design. First, Clojure programs can be compiled as jar files and run interactively with any other Java program. In the context of knowledge management and semantic uses, fully 60% of existing applications can now interoperate with Clojure apps [8], an instant boon for leveraging many open source capabilities. Second, certain advantages from Java, including platform independence and the leverage of debugging and profiling tools, among others (see below), are gained. And, third, this same design approach has been applied to integrating with JavaScript (via ClojureScript) and the Common Language Runtime execution engine of Microsoft’s .Net Framework (via Clojure CLR), both highly useful for Web purposes and as exemplars for other integrations;
  2. Scalable performance — by leveraging Java’s multithreaded and concurrent design, plus a form of caching called memoization in conjunction with the lazy evaluation noted above, Clojure gains significant performance advantages and scalability. The immutability of Clojure data has minimal data access conflicts (it is thread-safe), further adding to performance advantages. We (SD) have yet to require such advantages, but it is a comfort to know that large-scale datasets and problems likely have a ready programmatic solution when using Clojure;
  3. More Lispiness — Clojure extends the basic treatment of Lisp’s s-expressions to maps and vectors, basically making all core constructs into extensible abstractions; Clojure is explicit in how metadata and reifications can be applied using Lisp principles, really useful for semantic applications; and Clojure EDN (Extensible Data Notation) was developed as a Lisp extension to provide an analog to JSON for data specification and exchange using ClojureScript [9]. SD, in turn, has taken this approach and extended it to work with the OSF Web services [10]. These extensions go to the core of the Lisp mindset, again reaffirming the malleability and extensibility of Lisp;
  4. Process-oriented — many knowledge management tasks are themselves the results of processing pipelines, or are semi-automatic in nature and require interventions by users or subject matter experts in filtering or selecting results. Knowledge management tasks and the data pre-processing that precedes them often require step-wise processes or workflows. The immutability of data and functions in Lisp means that state is also immutable. Clojure takes advantage of these Lisp constructs to make explicit the treatment of time and state changes. Further, based on Hickey’s background in scheduling systems, a construct of transducers [11] is being introduced in version 1.70. The design of these systems is powerful for defining and manipulating workflows and rule-based branching. Data migration and transformations benefit from this design; and
  5. Active and desirable — hey, developers want to work with this stuff and think it is fun. SD’s clients and partners are also desirous of working with program languages attuned to knowledge management problems, diverse native data, and workflows controlled and directed by knowledge workers themselves. These market interests are key affirmations that Clojure, or dialects similar to it, are the wave of the future for knowledge management programming. Combined with its active developer community, Clojure is our choice for KM applications for the foreseeable future.
Clojure for Knowledge Apps

I’m sure had this article been written from a developer’s perspective, different emphases and different features would have arisen. There is no perfect programming language and, even if there were, its utility would vary over time. The appropriateness of program languages is a matter of context. In our context of knowledge management and artificial intelligence applications, Clojure is our due diligence choice from a business-level perspective.

There are alternatives to the points raised herein, like Scheme, Erlang or Haskell. Scala offers some of the same JVM benefits as noted. Further, tooling for Clojure is still limited (though growing), and it requires Java to run and develop. Even with extensions and DSLs, there is still the initial awkwardness of learning Lisp’s mindset.

Yet, ultimately, the success of a programming language is based on its degree of use and longevity. We are already seeing very small code counts and productivity from our use of Clojure. We are pleased to see continued language dynamism from such developments as Transit [9] and transducers [11]. We think many problem areas in our space — from data transformations and lifting, to ontology mapping, and then machine learning and AI and integrations with knowledge bases, all under the control of knowledge workers versus developers — lend themselves to Clojure DSLs of various sorts. We have plans for these DSLs and look forward to contribute them to the community.

We are excited to now find an aesthetic programming fit with our efforts in knowledge management. We’d love to see Clojure become the go-to language for knowledge-based applications. We hope to work with many of you in helping to make this happen.

[1] I have also been involved with the development of two new languages, Promula and VIQ, and conducted due diligence on C#, Ruby and Python, but none of these languages were ultimately selected. [2] Native apps on smartphones are likely going through the same transition. [3] As of the date of this article, Clojure is in version 1.60. [4] See M. K. Bergman, 2009. ” The Open World Assumption: Elephant in the Room,” December 21, 2009. The open world assumption (OWA) generally asserts that the lack of a given assertion or fact being available does not imply whether that possible assertion is true or false: it simply is not known. In other words, lack of knowledge does not imply falsity. Another way to say it is that everything is permitted until it is prohibited. OWA lends itself to incremental and incomplete approaches to various modeling problems. OWA is a formal logic assumption that the truth-value of a statement is independent of whether or not it is known by any single observer or agent to be true. OWA is used in knowledge representation to codify the informal notion that in general no single agent or observer has complete knowledge, and therefore cannot make the closed world assumption. The OWA limits the kinds of inference and deductions an agent can make to those that follow from statements that are known to the agent to be true. OWA is useful when we represent knowledge within a system as we discover it, and where we cannot guarantee that we have discovered or will discover complete information. In the OWA, statements about knowledge that are not included in or inferred from the knowledge explicitly recorded in the system may be considered unknown, rather than wrong or false. Semantic Web languages such as OWL make the open world assumption. Also, you can search on OWA on this blog. [5] Paul Graham, 1993. “Programming Bottom-Up,” is a re-cap on Graham’s blog related to some of his earlier writings on programming in Lisp. By “bottom up” Graham means “. . . changing the language to suit the problem. . . . Language and program evolve together.” [6] A really nice explanation of this approach is in James Donelan, 2013. “Code is Data, Data is Code,” on the Mulesoft blog, September 26, 2013. [7] Rich Hickey is a good public speaker. Two of his seminal videos related to Clojure are “Are We There Yet?” (2009) and “Simple Made Easy” (2011). [8] My Sweet Tools listing of knowledge management software is dominated by Java, with about half of all apps in that language. [9] See the Clojure EDN; also Transit, EDN’s apparent successor. [10] structEDN is a straightforward RDF serialization in EDN format. [11] For transducers in Clojure version 1.70, see this Hickey talk, “Transducers” (2014).

New OSF version 3.1 and OSF Web Site

AI3:::Adaptive Information (Mike Bergman) - Tue, 12/09/2014 - 19:07

Structured Dynamics has just released version 3.1 of the Open Semantic Framework and announced the update of the OSF Web site. OSF is an integrated software stack using semantic technologies for knowledge management. It has a layered architecture that combines existing open source software with additional open source components. OSF is made available under the Apache 2 license.

Enhancements to OSF version 3.1 include:

  • Upgrade to use the Virtuoso Open Source version 7.1.0 triple store;
  • A new API for Clojure developers: clj-osf;
  • A sandbox where each of the nearly 30 OSF Web services may be invoked and tested;
  • A variety of bug fixes and minor functional improvements; and
  • An updated and improved OSF installer.

OSF version 3.1 is available for download from GitHub.

More details on the release can be found on Frédérick Giasson’s blog. Fred is OSF’s lead developer. William (Bill) Anderson also made key contributions to this release.

Knowledge-based Artificial Intelligence

AI3:::Adaptive Information (Mike Bergman) - Mon, 11/17/2014 - 15:56
What Goes Around, Comes Around, Only Now with Real Knowledge

A recent interview with a noted researcher, IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley, provided a downplayed view of recent AI hype. Jordan was particularly critical of AI metaphors to real brain function and took the air out of the balloon about algorithm advances, pointing out that most current methods have roots that are decades long [1]. In fact, the roots of knowledge-based artificial intelligence (KBAI), the subject of this article, also extend back decades.

Yet the real point, only briefly touched upon by Jordan in his lauding of Amazon’s recommendation service, is that the dynamo in recent AI progress has come from advances in the knowledge and statistical bases driving these algorithms. The improved digital knowledge bases behind KBAI have been the power behind these advances.

Knowledge bases are finally being effectively combined with AI, a dynamic synergy that is only now being recognized, let alone leveraged. As this realization increases, many forms of useful information structure in the wild will begin to be mapped to these knowledge bases, which will further extend the benefits we are are now seeing from KBAI.

Knowledge-based artificial intelligence, or KBAI, is the use of large statistical or knowledge bases to inform feature selection for machine-based learning algorithms used in AI. The use of knowledge bases to train the features of AI algorithms improves the accuracy, recall and precision of these methods. This improvement leads to perceptibly better results to information queries, including pattern recognition. Further, in a virtuous circle, KBAI techniques can also be applied to identify additional possible facts within the knowledge bases themselves, improving them further still for KBAI purposes.

It is thus, in my view, the combination of KB + AI that has led to the notable AI breakthroughs of the past, say, decade. It is in this combination that we gain the seeds for sowing AI benefits in other areas, from tagging and disambiguation to the complete integration of text with conventional data systems. And, oh, by the way, the structure of all of these systems can be made inherently multi-lingual, meaning that context and interpretation across languages can be brought to our understanding of concepts.

Structured Dynamics is working to democratize a vision of KBAI that brings its benefits to any enterprise, using the same approaches that the behemoths of the industry have used to innovate knowledge-based artificial intelligence in the first place. How and where the benefits of such KBAI may apply is the subject of this article.

A Brief History of Knowledge-based Systems

Knowledge-based artificial intelligence is not a new idea. Its roots extend back perhaps to one of the first AI applications, Dendral. In 1965, nearly a half century ago, Edward Feigenbaum initiated Dendral, which became a ten-year effort to develop software to deduce the molecular structure of organic compounds using scientific instrument data. Dendral was the first expert system and used mass spectra or other experimental data together with a knowledge base of chemistry to produce a set of possible chemical structures. This set the outline for what came to be known as knowledge-based systems, which are one or more computer programs that reason and use knowledge bases to solve complex problems.

Indeed, it was in the area of expert systems that AI first came to the attention of most enterprises. According to Wikipedia,

Expert systems were designed to solve complex problems by reasoning about knowledge, represented primarily as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of AI software.

Expert systems spawned the idea of knowledge engineers, whose role was to interview and codify the logic of the chosen experts. But, expert systems proved to be expensive to build and difficult to maintain and tune. As the influence of expert systems waned, another branch emerged, that of knowledge-based engineering and their support for CAD- and CASE-type systems. Still, overall penetration to date of most knowledge-based systems can most charitably be described as disappointing.

The specific identification of “KBAI” was (to my knowledge) first made in a Carnegie-Mellon University report to DARPA in 1975 [2]. The source knowledge bases were broadly construed, including listings of hypotheses. The first known patent citing knowledge-based artificial intelligence is from 1992 [3]. Within the next ten years there were dedicated graduate-level course offerings on KBAI at many universities, including at least Indiana University, SUNY Buffalo, and Georgia Tech.

In 2007, Bossé et al. devoted a chapter to KBAI in their book on information fusion, but still, at that time, the references were more generic [4]. However, by 2013, the situation was changing fast, as this quote from Hovy et al. indicates [5]:

“Recently, however, this stalemate [the so-called ‘knowledge acquisition bottleneck] has begun to loosen up. The availability of large amounts of wide-coverage semantic knowledge, and the ability to extract it using powerful statistical methods, are enabling significant advances in applications requiring deep understanding capabilities, such as information retrieval and question-answering engines. Thus, although the well-known problems of high cost and scalability discouraged the development of knowledge-based approaches in the past, more recently the increasing availability of online collaborative knowledge resources has made it possible to tackle the knowledge acquisition bottleneck by means of massive collaboration within large online communities. This, in turn, has made these online resources one of the main driving forces behind the renaissance of knowledge-rich approaches in AI and NLP – namely, approaches that exploit large amounts of machine-readable knowledge to perform tasks requiring human intelligence.” (citations removed)

The waxing and waning of knowledge-based systems and its evolution over fifty years have led to a pretty well-defined space, even if not all component areas have achieved their commercial potential. Besides areas already mentioned, knowledge-based systems also include:

  • Knowledge models — formalisms for knowledge representation and reasoning, and
  • Reasoning systems — software that generates conclusions from available knowledge using logical techniques such as deduction and induction.

We can organize these subdomains as follows. Note particularly that the branch of KBAI (knowledge-based artificial intelligence) has two main denizens: recognized knowledge bases, such as Wikipedia, and statistical corpora. The former are familiar and evident around us; the latter are largely proprietary and not (generally) publicly accessible:

Some prominent knowledge bases and statistical corpora are identified below. Knowledge bases are coherently organized information with instance data for the concepts and relationships covered by the domain at hand, all accessible in some manner electronically. Knowledge bases can extend from the nearly global, such as Wikipedia, to very specific topic-oriented ones, such as restaurant reviews or animal guides. Some electronic knowledge bases are designed explicitly to support digital consumption, in which case they are fairly structured with defined schema and standard data formats and, increasingly, APIs. Others may be electronically accessible and highly relevant, but the data is not staged in a easily-consumable way, thereby requiring extraction and processing prior to use.

The use and role of statistical corpora is harder to discern. Statistical corpora are organized statistical relationships or rankings that facilitate the processing of (mostly) textual information. Uses can range from entity extraction to machine language translation. Extremely large sources, such as search engine indexes or massive crawls of the Web, are most often the sources for these knowledge sets. But, most are applied internally by those Web properties that control this big data.

The Web is the reason these sources — both statistical corpora and knowledge bases — have proliferated, so the major means of consuming them is via Web services with the information defined and linked to URIs.

My major thesis has been that it is the availability of electronically accessible knowledge bases, exemplified and stimulated by Wikipedia [6], that has been the telling factor in recent artificial intelligence advances. For example, there are at least 500 different papers that cite using Wikipedia for various natural language processing, artificial intelligence, or knowledge base purposes [7]. These papers began to stream into conferences about 2005 to 2006, and have not abated since. In turn, the various techniques innovated for extracting more and more structure and information from Wikipedia are being applied to other semi-structured knowledge bases, resulting in a true renaissance of knowledge-based processing for AI purposes. These knowledge bases are emerging as the information substrate under many recent computational advances.

Knowledge Bases in Relation to Overall Artificial Intelligence

A few months ago I pulled together a bit of an interaction diagram to show the relationships between major branches of artificial intelligence and structures arising from big data, knowledge bases, and other organizational schema for information:

What we are seeing is a system emerging whereby multiple portions of this diagram interact to produce innovations. Take, for example, Apple‘s Siri [8], or Google’s Google Now or the many similar systems that have emerged on smartphones. Spoken instructions are decoded to text, which is then parsed and evaluated for intent and meaning and then posed to a general knowledge base. The text results are then modulated back to speech with the answer in the smartphone’s speakers. The pattern recognition at the front and back end of this workflow has been made better though statistical datasets derived from phonemes and text. The text understanding is processed via natural language processing and semantic technologies [16], with the question understanding and answer formulation coming from one or more knowledge bases.

This remarkable chain of processing is now almost taken for granted, though its commercial use is less than five years old. For different purposes with different workflows we see effective question answering and diagnosis with systems like IBM’s Watson [9] and structured search results from Google’s Knowledge Graph [10]. Try posing some questions to Wolfram Alpha and then stand back and be impressed with the data visualization. Behind the scenes, pattern recognition from faces to general images or thumbprints further is eroding the distinction between man and machine. Google Translate now covers language translation between 60 human languages [11] — and pretty damn effectively, too. All major Web players are active in these areas, from Amazon’s recommendation system [12] to Facebook [42], Microsoft [13], Twitter [14] or Baidu [15].

Though not universal, most all recent AI advances leveraging knowledge bases have utilized Wikipedia in one way or another. Even Freebase, the core of Google’s Knowledge Graph, did not really blossom as a separate data crowdsourcing concern until its former owner, Metaweb, decided to bring Wikipedia into its system. Many other knowledge bases, as noted below, are also derivatives or enhancements to Wikipedia in one way or another.

I believe the reasons for Wikipedia’s influence have arisen from its nearly global scope, its mix of semi-structured data and text, its nearly 200 language versions, and its completely open and accessible nature. Regardless, it is also certainly true that techniques honed with Wikipedia are now being applied to a diversity of knowledge bases. We are also seeing an appreciation start to grow in how knowledge bases can enhance the overall AI effort.

Useful Statistical and Knowledge Sources

The diagram on knowledge-based systems above shows two kinds of databases contributing to KBAI: statistical corpora or databases and true knowledge bases. The statistical corpora tend to be hidden behind proprietary curtains, and also more limited in role and usefulness than general knowledge bases.

Statistical Corpora

The statistical corpora or databases tend to be of a very specific nature. While lists of text corpora and many other things may contribute to this category, the ones actually in commercial use tend to be quite focused in scope, very large, and designed for bespoke functionality. A good example, and one that has been contributed for public use, is the Web 1T 5-gram data set [17]. This data set, contributed by Google for public use in 2006, contains English word n-grams and their observed frequency counts. N-grams capture word tokens that often coincide with one another, from single words to phrases. The length of the n-grams ranges from unigrams (single words) to five-grams. The database was generated from approximately 1 trillion word tokens of text from publicly accessible Web pages.

Another example of statistical corpora are what is used in Google’s Translate capabilities [11]. According to Franz Josef Och, who was the lead manager at Google for its translation activities and an articulate spokesperson for statistical machine translation, a solid base for developing a usable language translation system for a new pair of languages should consist of a bilingual text corpus of more than a million words, plus two monolingual corpora each of more than a billion words. Statistical frequencies of word associations form the basis of these reference sets. Google originally seeded its first language translators with multiple language texts from the United Nations [18].

Such lookup or frequency tables in fact can shade into what may be termed a knowledge base as they gain more structure. NELL, for example (and see below), contains a relatively flat listing of assertions extracted from the Web for various entities; it goes beyond frequency counts or relatedness, but does not have the full structure of a general knowledge base like Wikipedia [19]. We thus can see that statistical corpora and knowledge bases in fact reside on a continuum of structure, with no bright line to demark the two categories.

Nonetheless, most statistical corpora will never be seen publicly. Building them requires large amounts of input information. And, once built, they can offer significant commercial value to their developers to drive various machine learning systems and for general lookup.

Knowledge Bases

There are literally hundreds of knowledge bases useful to artificial intelligence, most of a restricted domain nature. Listed below, partially informed by Suchanek and Weikum’s work [20], are some of the broadest ones available. Note that many leverage or are derivatives of or extensions to Wikipedia:

  • BabelNet — is a multilingual lexicalized semantic network and ontology automatically created by linking Wikipedia to WordNet [21]
  • Biperpedia — is an ontology with 1.6M (class, attribute) pairs and 67K distinct attribute names, a totally unique resource, but one that is not publicly available [22]
  • ConceptNet — is a semantic network with concepts as nodes and edges that are assertions of common sense about these concepts [23]
  • Cyc — is an artificial intelligence project that attempts to assemble a comprehensive ontology and knowledge base of everyday common sense knowledge, with the goal of enabling AI applications to perform human-like reasoning [24]
  • DBpedia — extracts structured content from the information created as part of the Wikipedia, principally from its infoboxes [25]
  • DeepDive — employs statistical learning and inference to combine diverse data resources and best-of-breed algorithms in order to construct knowledge bases from hundreds of millions of Web pages [26]
  • EntityCube — is a knowledge base built from the statistical extraction of structured entities, named entities, entity facts and relations from the Web [27]
  • Freebase — is a large collaborative knowledge base consisting of metadata composed mainly by its community members, but centered initially on Wikipedia; Freebase is a key input component to Google’s Knowledge Graph [28]
  • GeoNames — is a geographical database that contains over 10,000,000 geographical names corresponding to over 7,500,000 unique features [29]
  • ImageNet — is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images [30]
  • KnowItAll — is a variety of domain-independent systems that extract information from the Web in an autonomous, scalable manner [31]
  • Knowledge Vault (Google) — is a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories; it is not publicly available [32]
  • LEVAN (Learning About Anything) — is a fully-automated approach for learning extensive models for a wide range of variations (e.g., actions, interactions, attributes and beyond) from images for any concept, leveraging the vast resources of online books [43]
  • NELL (Never-Ending Language Learning system) — is a semantic machine learning system developed at Carnegie Mellon University that identifies a basic set of fundamental semantic relationships between a few hundred predefined categories of data, such as cities, companies, emotions and sports teams [19]
  • Probase — is a universal, probabilistic taxonomy that contains 2.7 million concepts harvested from a corpus of 1.68 billion web pages [33]
  • UMBEL — is an upper ontology of about 28,000 reference concepts and a vocabulary for aiding that ontology mapping, including expressions of likelihood relationships [34]
  • Wikidata — is a common source of certain data types (for example, birth dates) which can be used by Wikimedia projects such as Wikipedia [35]
  • WikiNet — is a multilingual extension of the facts found in the multiple language versions of Wikipedia [36]
  • WikiTaxonomy — is a large-scale taxonomy derived from the category relationships in Wikipedia [37]
  • Wolfram Alpha — is a computational knowledge engine made available by subscription as an online service that answers factual queries directly by computing the answer from externally sourced “curated data”
  • WordNet — is a lexical database for the English language that groups words into sets of synonyms called synsets, provides short definitions and usage examples, and records a number of relations among these synonym sets or their members [38]
  • YAGO — is extracted from Wikipedia (e.g., categories, redirects, infoboxes), WordNet (e.g., synsets, hyponymy), and GeoNames [39].
What Work is Being Done and the Future

It is instructive to inspect what kinds of work or knowledge these bases are contributing to the AI enterprise. The most important contribution, in my mind, is structure. This structure can relate to the subsumption (is-a) or part of (mereology) relationships between concepts. This form of structure contributes most to understanding the taxonomy or schema of a domain; that is, its scaffolding of concepts. This structure helps orient the instance data and other external structures, generally through some form of mapping.

The next rung of contribution from these knowledge bases is in the nature of the relations between concepts and their instances. These form the predicates or nature of the relationships between things. This kind of contribution is also closely related to the attributes of the concepts and the properties of the things that populate the structure. This kind of information tends to be the kind of characteristics that one sees in a data record: a specific thing and the values for the fields by which it is described.

Another contribution from knowledge bases comes from identity and disamgibuation. Identity works in that we can point to authoritative references (with associated Web identifiers) for all of the individual things and properties in our relevant domain. We can use these identities to decide the “canonical” form, which also gives us a common reference for referring to the same things across information sources or data sets. We also gain the means for capturing the various ways that anything can be described, that is the synonyms, jargon, slang, acronyms or insults that might be associated with something. That understanding helps us identify the core item at hand. When we extend these ideas to the concepts or types that populate our relevant domain, we can also begin to establish context and other relationships to individual things. When we encounter the person “John Smith” we can use co-occurring concepts to help distinguish John Smith the plumber from John Smith the politician or John Smith the policeman. As more definition and structure is added, our ability to discriminate and disambiguate goes up.

Some of this work resides in the interface between schema (concepts) and the instances (individuals) that populate that schema, what I elsewhere have described as the work between the T-Box and A-Box of knowledge bases [40]. In any case, with richer understandings of how we describe and discern things, we can now begin to do new work, not possible when these understandings were lacking. We can now, for example, do semantic search where we can relate multiple expressions for the same things or infer relationships or facets that either allow us to find more relevant items or better narrow our search interests.

With true knowledge bases and logical approaches for working with them and their structure, we can begin doing direct question answering. With more structure and more relationships, we can also do so in rather sophisticated ways, such as identifying items with multiple shared characteristics or within certain ranges or combinations of attributes.

Structured information and the means to query it now gives us a powerful, virtuous circle whereby our knowledge bases can drive the feature selection of AI algorithms, while those very same algorithms can help find still more features and structure in our knowledge bases. The interaction between AI and the KBs means we can add still further structure and refinement to the knowledge bases, which then makes them still better sources of features for informing the AI algorithms:

Once this threshold of feature generation is reached, we now have a virtuous dynamo for knowledge discovery and management. We can use our AI techniques to refine and improve our knowledge bases, which then makes it easier to improve our AI algorithms and incorporate still further external information. Effectively utilized KBAI thus becomes a generator of new information and structure.

This virtuous circle has not yet been widely applied beyond the early phases of, say, adding more facts to Wikipedia, as some of our examples above show. But these same basic techniques can be applied to the very infrastructural foundations of KBAI systems in such areas as data integration, mapping to new external structure and information, hypothesis testing, diagnostics and predictions, and the myriad of other uses to which AI has been hoped to contribute for decades. The virtuous circle between knowledge bases and AIs does not require us to make leaps and bounds improvements in our core AI algorithms. Rather, we need only stoke our existing AI engines with more structure and knowledge fuel in order to keep the engine chugging.

The vision of a growing nexus of KBAI should also prove that efficiencies and benefits also increase through a power function of the network effect, similar to what I earlier described in the Viking algorithm [41]. We know how we can extract further structure and benefit from Wikipedia. We can see how such a seed catalyst can also be the means for mapping and relating more specific domain knowledge bases and structure. The beauty of this vision is that we already can see the threshold benefits from a decade of KBAI development. Each new effort — and there are many — will only act to add to these benefits, with each new increment contributing more than the increment that came before. That sounds to me like productivity, and a true basis for wealth creation.

[1] See Lee Gomes, 2014. “Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts,” in IEEE Spectrum, 20 Oct 2014. See also http://www.kdnuggets.com/2014/11/berkeley-michael-jordan-big-data-transformative-not-delusion.html. [2] Lee D. Erman and Victor R. Lesser, 1975. A Multi-level Organization for Problem Solving Using Many Diverse, Cooperating Sources of Knowledge, DARPA Report AD-AO12-919, Carnegie-Mellon University, Pittsburgh, Pa. 15213 , March, 1975, 24 pp. See http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=ADA012916. The authors followed this up with a 1978 book, System engineering techniques for artificial intelligence systems. Academic Press, 1978. [3] See “Method for converting a programmable logic controller hardware configuration and corresponding control program for use on a first programmable logic controller to use on a second programmable logic controller,” US Patent No 5142469 A, August 25, 1992. [4] Éloi Bossé, Jean Roy, Steve Wark, Eds., 2007. Concepts, Models, and Tools for Information Fusion, Artech House Publishers, 2007-02-28, SKU-13/ISBN: 9781596939349. Chapter 11 is devoted to “Knowledge-Based and Artificial Intelligence Systems.” [5] Eduard Hovy, Roberto Navigli, and Simone Paolo Ponzetto, 2013. “Collaboratively Built Semi-Structured Content and Artificial Intelligence: The Story So Far,” Artificial Intelligence 194 (2013): 2-27. See http://wwwusers.di.uniroma1.it/~navigli/pubs/AIJ_2012_Hovy_Navigli_Ponzetto.pdf. [6] See M.K. Bergman, “SWEETpedia,” listing of Wikipedia research articles, on AI3:::Adaptive Information blog, January 25, 2010. The listing as of its last update included 246 articles. [7] See the combined references between [6] and [20]; also, see Wikipedia’s own “Wikipedia in Academic Studies.” [8] Wade Roush, 2010. “The Story of Siri, from Birth at SRI to Acquisition by Apple–Virtual Personal Assistants Go Mobile.” Xconomy. com 14 (2010). [9] Special Issue on “This is Watson“, 2012. IBM Journal of Research and Development 56(3/4), May/June 2012. See also this intro to Watson video. [10] Amit Singhal, 2012. ” Introducing the Knowledge Graph: Things, not Strings,” Google Blog, May 16, 2012 [11] Thomas Schulz, 2013. “Google’s Quest to End the Language Barrier,” September 13, 2013, Spiegel Online International. See http://www.spiegel.de/international/europe/google-translate-has-ambitious-goals-for-machine-translation-a-921646.html. Also see, Alon Halevy, Peter Norvig, and Fernando Pereira, 2009. “The Unreasonable Effectiveness of Data,” in IEEE Intelligent Systems, March/April 2009, pp 8-12. [12] Greg Linden, Brent Smith, and Jeremy York, 2003. “Amazon. com Recommendations: Item-to-item Collaborative Filtering,” Internet Computing, IEEE 7, no. 1 (2003): 76-80. [13] Athima Chansanchai, 2014. “Microsoft Research Shows off Advances in Artificial Intelligence with Project Adam,” on Next at Microsoft blog, July 14, 2014. Also, see [33]. [14] Jimmy Lin and Alek Kolcz, 2012. “Large-Scale Machine Learning at Twitter,” SIGMOD, May 20–24, 2012, Scottsdale, Arizona, US. See http://www.dcs.bbk.ac.uk/~DELL/teaching/cc/paper/sigmod12/p793-lin.pdf. [15] Robert Hof, 2014. “Interview: Inside Google Brain Founder Andrew Ng’s Plans To Transform Baidu,” Forbes Online, August 28, 2014. [16] Alok Prasad and Lee Feigenbaum, 2014, “How Semantic Web Tech Can Make Big Data Smarter,” in CMSwire, Oct 6, 2014. [17] The Web 1T 5-gram data set is available from the Linguistic Data Corporation, University of Pennsylvania. [18] Franz Josef Och, 2005. “Statistical Machine Translation: Foundations and Recent Advances,” The Tenth Machine Translation Summit, Phuket, Thailand, September 12, 2005. [19] Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka Jr, and Tom M. Mitchell, 2010. “Toward an Architecture for Never-Ending Language Learning,” in AAAI, vol. 5, p. 3. 2010. [20] Fabian M. Suchanek and Gerhard Weikum, 2014. “Knowledge Bases in the Age of Big Data Analytics,Proceedings of the VLDB Endowment 7, no. 13 (2014). [21] Roberto Navigli and Simone Paolo Ponzetto, 2012. “BabelNet: The Automatic Construction, Evaluation and Application of a Wide-coverage Multilingual Semantic Network,” Artificial Intelligence 193 (2012): 217-250. [22] Rahul Gupta, Alon Halevy, Xuezhi Wang, Steven Euijong Whang, and Fei Wu, 2014. “Biperpedia: An Ontology for Search Applications,” . Proceedings of the VLDB Endowment 7(7), 2014. [23] Robert Speer and Catherine Havasi, “Representing General Relational Knowledge in ConceptNet 5,” LREC 2012, pp. 3679-3686. [24] Douglas B. Lenat and R. V. Guha, 1990. Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project, Addison-Wesley, 1990 ISBN 0-201-51752-3. [25] Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives 2007. “DBpedia: A Nucleus for a Web of Open Data,” presented at ISWC 2007, Springer Berlin Heidelberg, 2007. [26] Feng Niu, Ce Zhang, Christopher Ré, and Jude W. Shavlik, 2012. “DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference,” in VLDS, pp. 25-28. 2012. [27] Zaiqing Nie, J-R. Wen, and Wei-Ying Ma, 2012. ” Statistical Entity Extraction From the Web,” in Proceedings of the IEEE 100, no. 9 (2012): 2675-2687. [28] Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor, 2008. “Freebase: a Collaboratively Created Graph Database for Structuring Human Knowledge,” in Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247-1250. ACM, 2008. [29] Mark Wick and Bernard Vatant, 2012. “The GeoNames Geographical Database,” available from the World Wide Web at http://geonames.org (2012). [30] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, 2009. ” ImageNet: A Large-scale Hierarchical Image Database,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 248-255. [31] Oren Etzioni, Michael Cafarella, Doug Downey, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, and Alexander Yates, 2005. “Unsupervised Named-entity Extraction from the Web: An Experimental Study,” Artificial Intelligence 165, no. 1 (2005): 91-134. [32] Xin Luna Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun and Wei Zhang, 2014. “Knowledge Vault: a Web-scale Approach to Probabilistic Knowledge Fusion,” KDD 2014. [33] Wentao Wu, Hongsong Li, Haixun Wang, and Kenny Q. Zhu, 2012. “Probase: A Probabilistic Taxonomy for Text Understanding,” in Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 481-492. ACM, 2012. [34] M.K. Bergman, 2008. “UMBEL: A Subject Concepts Reference Layer for the Web”, Slideshare, July 2008. [35] Denny Vrandečić and Markus Krötzsch, 2014. “Wikidata: A Free Collaborative Knowledgebase,” Communications of the ACM 57, no. 10 (2014): 78-85. [36] Vivi Nastase and Michael Strube. 2013. “Transforming Wikipedia into a Large Scale Multilingual Concept Network,” Artificial Intelligence 194 (2013): 62-85. [37] Simone Paolo Ponzetto, and Michael Strube, 2007. “Deriving a Large Scale Taxonomy from Wikipedia,” in AAAI, vol. 7, pp. 1440-1445. 2007. [38] Christiane Fellbaum and George Miller, eds., 1998. WordNet: An Electronic Lexical Database, MIT Press, 1998. [39] Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, and Gerhard Weikum, 2013. “YAGO2: a Spatially and Temporally Enhanced Knowledge Base from Wikipedia,” Artificial Intelligence 194 (2013): 28-61; and Fabien M. Suchanek, Gjergji Kasneci, Gerhard Weikum, 2007. “YAGO: a Core of Semantic Knowledge,” in Proceedings of the 16th international Conference on World Wide Web, pp. 697-706. ACM, 2007. [40] See the T-Box and A-Box discussion with particular reference to artificial intelligence on M.K. Bergman, 2014. “Big Structure and Data Interoperability,” on AI3:::Adaptive Information blog, August 18, 2014. [41] M.K. Bergman, 2014. “The Value of Connecting Things – Part II: The Viking Algorithm,” on AI3:::Adaptive Information blog, September 14, 2014. [42] Cade Metz, 2013. “Facebook Taps ‘Deep Learninig’ Giant for New AI Lab,” on Wired.com, December 9, 2013. [43] Santosh Divvala, Ali Farhadi and Carlos Guestrin, 2014. “Learning Everything about Anything: Webly-Supervised Visual Concept Learning,” in CVPR 2014.

Peg Wins International Award

AI3:::Adaptive Information (Mike Bergman) - Thu, 10/16/2014 - 20:24

Peg, the well-being indicator system for the community of Winnipeg, recently won the international Community Indicators Consortium Impact Award, presented in Washington, D.C. on Sept. 30. Peg is a joint project of the United Way of Winnipeg (UWW)  and the International Institute for Sustainable Development (IISD). Our company, Structured Dynamics, was the lead developer for the project, which is also based on SD’s Open Semantic Framework (OSF) platform.

Peg is an innovative Web portal that helps identify and, on an ongoing basis, track indicators that relate to the economic, environmental, cultural and social well-being of the people of Winnipeg. Datasets may be selected and compared with a variety of charting and mapping and visualization tools at the level of the entire city, neighborhoods or communities. All told, there are now more than 60 indicators within Peg ranging from active transportation to youth unemployment rates representing thousands of individual records and entities. We last discussed Peg upon its formal release in December 2013.

Congratulations to all team members!

Pulse: Let My Wolfram Alpha Go

AI3:::Adaptive Information (Mike Bergman) - Thu, 10/09/2014 - 04:33

I have been dazzled by what Stephen Wolfram has been doing with Wolfram Alpha since Day 1. The mathematical capabilities are exceedingly impressive, the backing knowledge base is exceedingly impressive, and the visualization has also come to be exceedingly impressive. The problem: no open source or anything.

I’ve never met Wolfram and acknowledge he has seen success many times greater than most. But, putting on my VC cap, I see an old school adherence to closed and proprietary.

I would not presume to suggest where the Wolfram folks should draw these lines on open and closed, but I definitely do counsel they work hard to open up as much as they can; it is in their own self interest. The business model of today is premised on leveraging the network effect in various ways. Total proprietary is a turn off and drives the compounding basis for the network effect into the dirt.

Maybe their recent pricing initiatives with Mathematica online are a wink toward this direction. But, better still find a core of functionality and knowledge base and release it as open source. The world will beat a path to what created all of this impressive stuff in the first place.

Pulse: More SemWeb Retrospective

AI3:::Adaptive Information (Mike Bergman) - Tue, 09/30/2014 - 14:41

A few weeks back I reflected on more than a decade of involvement in the semantic Web. I appreciate the many nice comments and compliments received.

In part in reaction to that post and also because it is the retrospective season, Amit Sheth has just posted his own retrospective on the semWeb going back 15 years. Amit has been one of the leaders in the space and (according to his history) the first to obtain a semantic Web patent.

Amit’s wonderful history is more global and informed than my own, and I recommend you check it out. BTW, Amit is asking for comments from those involved in the early years for any corrections or additions.

Pulse: Pure v Mixed Seeds on Pandora

AI3:::Adaptive Information (Mike Bergman) - Mon, 09/29/2014 - 15:44

For years now I have been a huge fan of Pandora (sorry, not apparently available to all across the world due to digital rights issues). Even though Pandora’s Music Genome was not set up as a W3C-compliant ontology, in its use and application it is effectively one. What Pandora shows is that feature selection and characterization trumps language and data structure format.

Given that, I have also been a Web scientist as to how I select and promote music to meet my musical interests.

Thus, based on my own totally unscientific study, here are a few things I’ve found worth passing on. It would be bold to call them secrets; they are really more just observations:

  • When a new “seed” artist is chosen, the attributes of that artist (up to 450 different attributes from beat to genre to dominant instruments) set the pure characteristics of that channel
  • As similar songs play that meet this profile, when you vote them “up” or “down” you are effectively adding or deleting options for these 450 attributes in your profile criteria
  • Thus, continuous expressions of preference as a channel plays acts to “dilute” the purity of the initial seed; these preference expressions lead to a “mixed” seed
  • The more that choices are preferred, the more the signal of your original selection gets diluted.

The net result is that I now no longer vote any of my songs on any channel as up or down. Rather, I look to the purest “seeds” that capture my mode or genre preference. If my initial selection does not provide this purity, I delete it, and try to find a better true seed.

This approach has led to some awesome channels for me, that I can then combine together, depending on mood, into mixed shuffle play with randomized channel selection. I no longer vote any song up or down; I rather look for more telling seeds.

As a couple of examples, here is a channel of less-well known 60′s goldies and a jazz guitar channel that are pure, single-seed channels, that, at least for me, provide hours of consistent music in that genre. Roll your own!

Pulse: Big Data Smokes SemWeb

AI3:::Adaptive Information (Mike Bergman) - Thu, 09/25/2014 - 06:22

We all know the slow decline and zero take-off about the semantic Web writ large, but what we probably did not know is that Big data would gobsmack the concept. Trends do not lie.

Some ten years ago the idea of the semantic Web had about a 4x advantage in Google searches compared to that for “big data”. Today, Big data searches exceed those for the semWeb by 25-fold. The cross-over point occurred in about April 2011:

Within a year, projections are that the Big data interest level will exceed the semantic Web by 35x.

These may not be entirely apples-to-apples cases, but trends don’t often lie. Play yourself with these comparisons on Google Trends.

The Wall of (Big) Structure

AI3:::Adaptive Information (Mike Bergman) - Mon, 09/15/2014 - 05:46
Big Structure has a Foundation in Reference Structures, But Any Structure Aids Interoperability

Big Structure is built on a foundation of reference structures, with domain structures capturing the domain at hand. These represent the target foundations for mapping schema and transforming data in the wild into an operable, canonical form. Any structure, even the most lightweight of lists and metadata, can contribute to and be mapped into this model, as this wall of structure shows:

Described below are some of these structures, in rough descending order of completeness and usefulness, for making data interoperable. Please note that any of these structures might be available as linked data.

Reference Structures

In both semantics and artificial intelligence — and certainly in the realm of data interoperability — there is always the problem of symbol grounding. In the conceptual realm, symbol grounding means that when we use a term or phrase we are referring to the same thing. In the data value realm, symbol grounding means that when we refer to an object or a number, we are referring to the same measure.

UMBEL is the standard reference ontology used by Structured Dynamics. It contains 28,000 concepts (classes and relationships) derived from the Cyc knowledge base. The reference concepts of UMBEL are mapped to Wikipedia, schema.org (used in Google’s knowledge graph), DBpedia ontology classes, GeoNames and PROTON. Similar reference structures are used to ground the actual data values and attributes.

Other reference structures may be used, so long as they are rather complete in scope and coherent in their relationships. Logical consistency is a key requirement for grounding.

Knowledge Bases

Knowledge bases combine schema with data in a logical manner; well-constructed ones support computations, inference and reasoning. To date, the two primary knowledge bases that we use are Wikipedia and Cyc. However, many specific domain knowledge bases also exist.

Knowledge bases are important sources for symbol grounding. It addition, because of their computability, they may be used with artificial intelligence methods to both extend the knowledge base and to refine the feature estimates used in the AI algorithms.

Domain Ontologies

Domain ontologies, constructed as graphs, are the principal working structures in data interoperability. Though best practices recommend they be grounded in the reference structures, the domain structures are the ones that specifically capture the concepts and data attributes of the target information domain. More effort should be focused at this level in the wall of structure than any other.

Domain structures provide unique benefits in discovery, flexible access, and information integration due to their inherent connectedness. Further, these domain structures can be layered on top of existing information assets, which means they are an enhancement and not a displacement for prior investments. And, these domain structures may be matured incrementally, which means their development is cost-effective.


Data and schema in the wild need to be mapped and transformed into these canonical structures. What is known as data wrangling is an aspect of these mappings and transformations. Mappings thus become the glue that ties native data to interoperable forms.

Mapping is the critical bridging function in data interoperability. It requires tools and background intelligence to suggest possible correspondences; how well this is done is a key to making the semi-automatic mapping process as efficient as possible. Mapping structures are the result of the final correspondences. Mapping effort is a function of the scope of Big Structure, not the volume of Big Data.

Existing Structures

A broad variety of structures occur in the wild — from database schema and taxonomies to dictionaries and lists — that need to be represented in a common form and then mapped in order to support interoperability. The common representation used by Structured Dynamics is the RDF data model.

Structure Scripts

Scripting and tooling are essential to help create Big Structure efficiently.

Editor’s Note: We are pleased to share with you in advance some of the text from Structured Dynamics’ new Web site.

UMBEL Version 1.10 Released

AI3:::Adaptive Information (Mike Bergman) - Tue, 09/09/2014 - 15:10
Much Clean-up, Consistency Brought to New Version

Structured Dynamics today released version 1.10 of its open source UMBEL (Upper Mapping and Binding Exchange Layer) reference concept ontology. This new release is in preparation for a series of subsequent releases planned over the next few months as additional consistency and functionality is brought to the system.

This is the first UMBEL version to be created and loaded from scratch using a new Clojure scripting framework. Fred Giasson describes these scripts and highlights some of the new functionality in a separate blog post.

Besides this new processing framework, here are the key changes at a high level made in this new version:

  • Reconciliation with the parent OpenCyc concepts
  • Added reference concept definitions where missing
  • Added additional altLabels (to the semsets) in many cases
  • Checked graph integrity for relationships between concepts
  • Reviewed and corrected prefLabels to make them unique (for more usefulness in autocompletion)
  • Checked assignments of all reference concepts to a parent SuperType
  • Reviewed SuperType assignment inconsistencies and removed some disjoint assertions
  • Updated mappings to OpenCyc, GeoNames, schema.org and the DBpedia ontology (see next).

This new UMBEL v. 1.10 is now updated and consistent with OpenCyc (version 4.0, dated October 12, 2012), GeoNames (version 3.1, dated October 29, 2012), schema.org (version 1.9, dated August 19, 2014) and the DBpedia ontology (version 3.9, retrieved August 18, 2014). The resulting mappings now are:

  • 26,091 Reference Concepts in UMBEL Core
  • 1,925 Reference Concepts in UMBEL Geo
  • 27,691 links between OpenCyc and UMBEL (which includes Core & Geo)
  • 754 links between Schema.org classes and UMBEL
  • 688 links between Geonames.org classes and UMBEL
  • 682 links between the DBpedia ontology classes and UMBEL.

These changes have also resulted in some improvements to the umbel.org Web site and its Web services, as more fully described by Fred. The updated ontologies and mappings may be found at the UMBEL GitHub site.

What’s In the Pipeline

As noted, this version update is in preparation for some pending activities, which are now moving toward completion. Subsequent releases (perhaps not in the order shown) will include:

  • Introduction of a new reference Attributes Ontology as a new module extension to UMBEL
  • Completion of the mappings to the English Wikipedia
  • Adding new concepts resulting from these Wikipedia mappings, plus adding concepts to complete 100% mappings to GeoNames, DBpedia and schema.org
  • Additional definition and semset updates to the structure
  • Checks to external mappings for consistency
  • Automated tests for completing the integrity of the UMBEL graph by identifying missing connecting concepts
  • An improved method to extend disjointedness assertions across the UMBEL structure
  • Additional Web services to support the above.

Look here and on the UMBEL mailing list for these future announcements.

The Value of Connecting Things – Part III: Ten Benefits from Big Structure

AI3:::Adaptive Information (Mike Bergman) - Thu, 09/04/2014 - 15:17
Benefits Can be Gained Incrementally, and Are Cumulative

In the earlier installments of this article series we first described how to estimate the value of connections amongst Big Data datasets, premised on the network effect. We then went into detail in the second part about the Viking algorithm (VKG) derived to capture the Value of Knowledge Graphs.

In this concluding part of the series we summarize the use and implications of the Viking algorithm on your Big Data planning. We do so by offering ten guidelines for how including Big Structure may be leveraged in the context of a knowledge graph. We then conclude the series with some caveats on the interpretation of these results and a discussion of possible future directions.

Ten Guidelines for Big Structure

As you think through your Big Data initiatives, we recommend you keep these ten guidelines relating to data structure in mind:

  1. More structure always provides benefits — adding structure always provides a multiplier effect in value
  2. Making connections is more valuable than adding more data — Big Data alone is practically worthless if not connected. Adding more records has an additive effect on the value of the datasets in comparison to the multiplier effects of structure
  3. Benefits of structure increases with increasing dataset sizes (scale) — the multiplier effect of more structure (connections) increases with scale. Big Data projects are thus perfect candidates for consciously “connecting the dots”
  4. Particular kinds of structure — such as types or categorization — have higher benefit than annotations — structural characteristics at the record level that enable cross-dataset selections and comparisons are inherently more valuable than record-specific annotations. Typing of records into entity types is a very powerful lever
  5. The potential value of a knowledge graph depends on the nature of the domain — knowing what kind of knowledge graph is in play is an important metric for being able to estimate potential value from connections. Further, by adding connections (correct and coherent) it may also be possible to move the entire structure to a lower average degree of separation (D), with further multiplier benefits
  6. Structure can be added incrementally, and is cumulative — because these additions to structure are based on the open world assumption (OWA), it is possible to add structure incrementally. OWA enables connection and structuring efforts to be accomplished as budgets allow. But, because these structural benefits are cumulative (and with multipliers), later contributions can have increasing benefits over earlier ones
  7. Data wrangling is justified as a means to increase the accuracy of fact assertions — data wrangling should not be viewed as an overall “cost” to the effort, but as a key means for achieving the multiplier benefits arising from structure and connections
  8. Adding structure at the time of data wrangling is a cost-effective approach — the corollary to standard data wrangling is the wisdom of explicitly including structuring and connection to the efforts. The multiplier benefits that accrue are a means to markedly lower the marginal costs of data wrangling in relation to realized benefits
  9. Ontologies provide inferred capabilities — though many kinds of structure can contribute to the Big Structure effort, ontologies, because of their logical structure, can be used to derive inferred “facts” in essence “for free.” (And remember, more “facts” are the basis for the multiplier benefits.) Inference provides a powerful means to leverage existing connections without explicitly needing to assert new ones
  10. Ontologies are the most preferred means for Big Structure — besides inference, ontologies set a structural framework of relationships (schema) very useful to helping to guide the nature of connections made. Also, ontologies can provide the conceptual and descriptive richness useful for tagging and other structure-adding activities [1]. Because of these advantages and their testable nature based in logic, ontologies represent the pinnacle of structural forms to achieve these value benefits.

Of course, mere connections for structure’s sake is silly. It is important that the structure added and connections made are correct, consistent and coherent. Even then, not all types of connections are created equal, with typing the most important, annotations the least.

The good thing is that Big Structure can be added as a slight increase over standard data wrangling efforts, and with much greater impact than standard wrangling. Further, the structures themselves, preferably guided by domain ontologies, are a means of testing these factors for subsequent structure additions. Not only does adding structure get easier with a foundation of existing structure, but it increases the value of the information by orders of magnitude.

Not the Last Word

Roughly twenty years ago Metcalfe’s law triggered a gold rush in trying to achieve network effects at Internet scale. Though the algorithm proved too optimistic at larger scales, the idea of the benefit from connections was firmly established. Ten years ago it was clear that some form of diminishing returns needed to be applied to connections at scale. Zipf’s law was not a bad guess, though we have subsequently learned that more graph-centric measures are more appropriate and accurate for estimating value. Now, the Viking algorithm has emerged as the best estimator of the value of connections within Big Data.

I suspect we will see further improvements to the Viking algorithm as: 1) we come to better understand graph structures (including the effects of clusters and cliques); and 2) we learn to distinguish the different value of different types of connections [2]. We can already see that typing and categorization have better structural effects above annotations. We further can see that the correctness of asserted “facts” is a key to realizing the multiplier benefits of connections and structures. Thus, we should see improved means for screening and testing assertions for their accuracy at scale.

At this stage, what the Viking algorithm gives us is a defensible means for assessing the value of adding structure (through connections) to our datasets. We see these multiplier effects to be huge, and to compound to even still further benefits with scale. We also see that the most developed forms of structure — namely, ontologies — bring still further benefits in inference and testable coherence. All Big Structure efforts should be aiming to express all of the structural insights for the organization and its datasets into these ontological forms [1].

While our current proxy for value — namely, asserted “facts” — is useful, it would also be helpful to be able to translate these “fact” assertions into a monetary value. As we move down this path we will discover, again, that not all “facts” are created equal, and some have more monetary value than others. Transitioning our estimates of value to a monetary basis will help set parameters for the cost-benefit analysis of data collection and structurizing that is the ultimate basis for planning Big Data and Big Structure initiatives.

In the end, many things need to be analyzed to understand the impacts of each connection and structure metric on the value of the resulting graph. But, what today’s current understanding of the network effect and the Viking algorithm brings us is a better means to understand and quantify the benefits of connected information. By any measure, these value benefits are multiples of what we see for unconnected data, the multiples of which grow massively with the scale of the data and their connections.

Big Structure is fertile ground for bringing in the sheaves. Let the harvesting begin.

[1] Though not further discussed here, the ontologies also provide the means for tagging (providing structure) to unstructured documents, which also brings the multiplier benefits from structure. On the retrieval side, such structure also aids faceting and filtered “slicing and dicing” of underlying datasets, thereby improving retrieval efficacy. [2] As one of the first approaches to capture these nuances, see Mischa Dohler, Thomas Watteyne, Fabrice Valois and Jia-Liang Lu, 2008. Kumar’s, Zipf’s and Other Laws: How to Structure a Large-Scale Wireless Network?, published in Annales des Telecommunications – Annals of Telecommunications 63, 5-6 pp. 239-251. See http://hal.archives-ouvertes.fr/docs/00/40/58/67/PDF/Large_Scale_Networks_journal_FINAL.pdf.

The Value of Connecting Things – Part II: The Viking Algorithm

AI3:::Adaptive Information (Mike Bergman) - Wed, 09/03/2014 - 14:56
Big Structure Improves Big Data by Orders of Magnitude

Yesterday in the first part of this series we raised the important question of how to value connections made between data. At the Big Data scales represented, we prepared a Basic Facts case of up to 2 billion assertions. We are using asserted “facts” as our value proxy. We’ll talk more about value and caveats in the third part of our series, tomorrow.

We saw that early estimates of network effects, such as Metcalfe’s law, overestimate value at scale. We looked at Zipf’s law as a means to capture the diminishing value of connections given the distance between facts. In today’s article we will focus on these factors of interaction and potential value in the specific context of knowledge graphs. Knowledge graphs are Big Structure representations that capture the schematics, concepts and measures in any given knowledge domain (that is, any domain of human activity).

Since I first tried to address the value of knowledge networks some five years ago [1], I have been disturbed about a couple of things [2]. First, I felt that the exponential or geometric bases for estimating the value of information connections were not correct, both because they fail at scale and they don’t discriminate that some connections work and are more important, while others are trivial or don’t work. Capturing this law of diminishing value in a context that makes sense for knowledge bases was, I felt, the key to answering the value riddle.

I believe we have now, in this series, provided a compelling basis for solving that riddle, which also points the way to further improvements. This assertion is an exciting statement, in that we now may have a quantitative basis in hand for determining where and how to spend our monies for Big Data and Big Structure initiatives. Such quantitative tools are a huge boon to bring analytic rigor to the data collection and integration challenge.

Adding connections (“Big Structure“) to Big Data can increase the value of enterprise information from one to three orders of magnitude; the value also scales linearly with added structure (attributes).

This article shows that adding connections (“Big Structure“) at Big Data scales can increase the value of enterprise information from one (ten) to three (thousands) orders of magnitude. The magnitude of the value scales linearly with each added structure (attribute). These value multipliers from adding Big Structure are a tremendously cost-effective addition to standard data wrangling efforts.

The Value of Knowledge Graphs (VKG) Formulation

The recognition of the need for a law of diminishing returns to reflect the distance between facts or assertions is a central argument in the Briscoe-Odlyzko-Tilly formulation (see [3] directly, and the prior Part I discussion). Not all information is connected, and not all connected information is of equivalent worth. The implied question in these statements, however, is how to capture those differences?

The B-O-T (or sometimes, O-T) formulation does not choose a bad starting proxy for this diminishment law. Zipf’s law reflects many observed distributions in human objects, roughly equivalant to power law, Pareto (“80:20″) distributions or n log (n) diminishing returns with long-tail characteristics. Examples include Internet distributions (such as popularity of Web sites or search terms), human language distributions, income rankings, population distributions, etc. There is no question that Zipf’s law distributions are common and frequent.

The only problem with picking the Zipf’s law basis, however, is that there was absolutely no evidence that such occurred for information networks or knowledge graphs. Zipf’s law distributions tend to be statements across single types for a single attribute distribution. Graphs, we can safely say, are anything but this distribution. Connections and multiple types are the rule, not the exception.

So, maybe the B-O-T formulation was correct, and maybe it was not. There was no empirical evidence to support this assertion for knowledge graphs. And, there did not appear to be a compelling logic argument for relating Zipf’s law to graphs other than they are artifacts of human endeavor.

My discomfort in adopting this arbitrary B-O-T basis, even though solidly embedded in human experience, caused me to seek alternative ideas and explanations, but also ones that fulfilled the key structural insights of diminishing returns and non-equivalent assertions that were the focal points of B-O-T, all within a graph context.

The Starting Basis

The breakthough occurred when I discovered an obscure, un-cited paper by Yaakov Stein [4]. Stein, a network and signals processing researcher of the first rank [4], wrote his paper as a means to understand and quantify his experience of joining LinkedIn and expanding his network. He began without an account and documented his experience as he joined and expanded his network of contacts on LinkedIn. He charted direct links, and then meticulously looked at and recorded secondary and tertiary links.

His formulation recognized that the value to an individual user equaled raising the access to the entire network (1) for that user plus the diminishing benefit represented by the participating graph’s other participants as measured by average degree of separation (d). d is an inherent measure of the graph type.

Though his context was a social network, the basic observation obtains: relations diminish by distance within a graph, with average link distance (directly related to degree of separation) being the key relevance metric. Connected “facts” or “friends” is essentially the same thing. It is all about what is shared amongst graph nodes.

The usefulness of this approach is that it grounds the multiplier effect in an inherent characteristic of the source graph, its average degree of separation [5]. Like Zipf’s law, the degree of separation is a distance measure, but one grounded specifically in graphs. Here is the Stein formulation:

A graph with a degree of separation of 4, then, would exhibit a network-wide power factor of 5/4 (4/4 plus 1/4).

The Viking Algorithm

As applied to knowledge graphs, however, this formulation still has two problems. The first minor one is that the degree of separation parameter should be D (the average across structure) rather than d. The second substantive one is that a correction factor needs to be included that accounts for the probability that an assertion may be false. This factor, F, is 1 – the measured error rate.

The resulting algorithm we term the Value of Knowledge Graph formulation, or the VKG (Viking) algorithm. It is expressed like this:

F is meant to be analogous to F-measure, the combined precision and recall statistic for information retrieval and NLP tasks. F in the case of the Viking algorithm is also meant to be a combined statistic that represents the “accuracy” (verifiable truthfulness) of statements asserted in the graph. F is essentially an estimated value for the residual falsity for the average statement in a graph, after removal of all assertions that do not meet existing coherency, consistency or completeness tests. F is determined by sampling statements across the graph and manually testing for truthfulness (or in a logical sense, validity given the existing statements in the graph). An F of 1 signifies complete truthfulness (accuracy); an F of 0 represents complete falsity [6].

Viking in Relation to Other Network Estimators

Now, with this explanation of basis, we can again look at the value of the Viking (VKG) algorithm in comparison to those discussed in the first part of this series. Again on a logarithmic scale, here are those results:

Figure 1. Knowledge Network Estimates

Excluding the exponential and geometric multipliers (namely, the “laws” of Metcalfe and Reed) in the top two curves, this shows the Viking (VKG) algorithm to have higher value than the B-O-T (O-T) algorithm, both of which are considerably higher than the Basic Facts. However, because the Figure 1 above has a logarithmic scale, these differences are harder to discern.

Viking Benefits Over the Basic Facts

Now corrected with our assumed F factor, we can begin to tease out the value benefits of connecting “facts” versus the unconnected Basic Facts. As with any logarithmic function, we see that the value benefits from connections increase in a growing manner at larger scales. For example, as Figure 2 shows below, at a level of 1000 records, the benefits from connections are 7x greater than unconnected data. By the time the scale grows to 1 million or 500 million records, the value benefits of connections grows to 44x to 215x, respectively:

Figure 2. Percent Improvement from Connections Scales with Records Size

Benefits from connections increase as a power function at increasing scales.

Setting the VKG Factor D

But the potential value of connectedness is also a function of the general degree of information separation for the given domain. We are still in the early phases of gathering statistics for such things, but the table below summarizes what is known about the “standard” level of connectivity in various domains and applications. Note, in general, most any knowledge graph would have a D factor ranging from 2 to 8:

Category Degrees of Separation (D) Notes Food webs ~ 2 [7] Genetic differences ~ 3 [8] LinkedIn ~ 3 [4] Twitter 3.435 – 4.67 [9] Facebook 3.74 [10] Potential research collaborators ~ 4 [11] UMBEL ~ 5.2 [12] Social networks (general) ~ 6 Mobile ad hoc networks ~ 7 [13] Small-world networks (max) ~ 8 [14] Table 1. Degrees of Separation for Various Knowledge Networks

More tightly linked, cohesive domains tend to have the lower degrees of separation. It is also interesting to note that some social networks, like Twitter and Facebook, are also able to lower degrees of separation (in comparison to their nominal “social network” benchmark) by virtue of the nature of their service.

As experience is gained and with more research, I expect more estimates and more refined ones. Depending on the nature of the domain at hand, it should then be possible to pick the closest analog to use in the Viking valuation algorithm. Nonetheless, we already have a range and respective values to provide meaningful value estimates today.

Using the values in Table 1, we are thus able to plot the effects (again, log scale) of these various degrees of separation in terms of the “fact” assertions that can be made for our Big Data test dataset:

Figure 3. Nature of Knowledge Graph Affects Potential Network Value

At the nominal Big Data scales of 100,000 and 1,000,000 records, the value of data connections in comparison to the unconnected Basic Facts case shows these following value improvement multipliers:

Domain 100,000 Records 1,000,000 Records Food webs 203x 611x Genetic differences 38x 84x Twitter 23x 46x Facebook 17x 33x Potential research collaborators 14x 26x UMBEL 8x 12x Social networks (general) 5x 8x Mobile ad hoc networks 3x 5x Table 2. Multiplier (X) Improvements by Domain from Connections Over the Basic Facts

Of course, our “Big Data Example” from Part I was silent about the exact nature of its knowledge graph. Based on empircial experience to date, the benefits from connecting data that was previously unconnected should fall somewhere within the limits of Table 2. Even at rather low scales and more loosely-connected domains, the value improvements in making connections with data is many-fold. At larger scales for tighter networks, the multipliers can become astounding.

Adding Structure to the Underlying Data

Another implication that the Viking algorithm allows us to test is the comparative benefit from adding structure to our datasets. Actually, “adding structure” is not strictly correct; it is “structurizing” the data via characterizations, attributes and categorizations. Of course, not all structure is created equal. Assigning or classifying our records into types, for example, applies to all records across the datasets and provides powerful cross-record linkages. Adding annotations or metadata to single records provides much lower benefits.

When we add structure across datasets the value improvements are a linear percent, as this figure shows:

Figure 4. Adding Structure has a Linear Effect on Value

For our Big Data example, each across-dataset structure characterization adds about 25% to 30% value per structure. Adding four structural characterizations, for example, more than doubles the “facts” assertion value (~ 140%) to the datasets.

Preview of Last Part

The last part forthcoming tomorrow will summarize the implications from the Viking algorithm on the role and importance of Big Structure to your organization’s Big Data efforts. Some caveats and future directions will conclude the series.

[1] The two articles written at that time were, M.K. Bergman, 2009. Structure the World, in AI3:::Adaptive Information blog, August 3, 2009, and M.K. Bergman, 2009, The Law of Linked Data, AI3:::Adaptive Information blog, October 11, 2009. [2] See also the then-current state of analysis by Eric Hellman, 2009. Normal and Inverse Network Effects for Linked Data, published in his blog, October 15, 2009; see http://go-to-hellman.blogspot.com/2009/10/normal-and-inverse-network-effects-for.html. [3] Bob Briscoe, Andrew Odlyzko, and Benjamin Tilly, 2006. “Metcalfe’s Law is Wrong,” in IEEE Spectrum, July 2006. A copy may be viewed at http://www.cse.unr.edu/~yuksem/teaching/nae/reading/2006-briscoe-metcalfes.pdf. Odlyzko, and Tilly had published an earlier version, (sometimes the approach is shown as O-T in addition to B-O-T), and the basic form of the algorithm appears in a single Odlyzko paper. [4] Yaakov (J) Stein, 2009. The Value of Being Linked In, on his personal Web site, April 2009; see http://www.dspcsp.com/pubs/linkedin.pdf. Note that his empirical tests suggested a degree of separation for LinkedIn of 3. [5] The average degree of separation is simply the graph’s average path distance – 1. For an explanation of average path distance, see [12]. [6] F is a summed average value across all assertions within a knowledge graph. In information retrieval, F-measures are now being achieved that exceed 0.90 (90%). For the cases used herein, F is estimated at 0.85. Again, this parameter is measured after all standard coherency, consistency, and completeness tests are applied to the ontology. These tests routinely remove many false assertions and establish the basic integrity of the graph. This acceptance threshold is itself constantly improving as experience is gained with basic graph integrity tests. In other words, tomorrow’s thresholds will be higher than today’s. [7] Richard J. Williams, Eric L. Berlow, Jennifer A. Dunne, Albert-László Barabási, and Neo D. Martinez, 2002. Two Degrees of Separation in Complex Food Webs, in Proceedings of the National Academy of Sciences, 99 (20):12913-12916, September 16, 2002, doi:10.1073/pnas.192448799; see http://www.pnas.org/content/99/20/12913.full. [8] See http://blog.23andme.com/ancestry/three-genetic-degrees-of-separation/. [9] Reza Bakhshandeh, Mehdi Samadi, Zohreh Azimifar and Jonathan Schaeffer, 2011. Degrees of Separation in Social Networks, in Proceedings, The Fourth International Symposium on Combinatorial Search (SoCS-2011), 6 pp.; see http://www.aaai.org/ocs/index.php/SOCS/SOCS11/paper/viewFile/4031/4352; and Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon, 2010. What is Twitter, a Social Network or a News Media?, in Proceedings of the 19th International Conference on World Wide Web, April 26–30, 2010, Raleigh, North Carolina, pp. 591-600, ACM; see http://snap.stanford.edu/class/cs224w-readings/kwak10twitter.pdf. [10] Lars Backstrom et al., 2012. Four Degrees of Separation, Archiv.org, January 6, 2012; see http://arxiv.org/pdf/1111.4570.pdf. [11] Paweena Chaiwanarom, Ryutaro Ichise, and Chidchano Lursinsap, 2010. Finding Potential Research Collaborators in Four Degrees of Separation, pp. 399-410, in Longbing Cao, Jiang Zhong, and Yong Feng, eds., Advanced Data Mining and Applications, Springer Berlin Heidelberg, http://dx.doi.org/10.1007/978-3-642-17313-4_39. [12] See http://fgiasson.com/blog/index.php/2014/08/11/graph-analysis-of-a-big-structure-umbel/#Average-Path-Length-Distribution. [13] Maria Papadopouli and Henning Schulzrinne, 2000. Seven Degrees of Separation in Mobile ad hoc Networks, presented at Global Telecommunications Conference, 2000 (GLOBECOM’00), IEEE. Vol. 3; see http://www.huaxiaspace.net/academic/classes/wi02/cse294/20020222globecom2000.pdf. [14] Paolo Pin, 2006. Eight Degrees of Separation, in Nota di Lavoro, Fondazione Eni Enrico Mattei, No. 78.2006 see http://www.econstor.eu/bitstream/10419/74249/1/NDL2006-078.pdf.

The Value of Connecting Things – Part I: A Foundation Based on the Network Effect

AI3:::Adaptive Information (Mike Bergman) - Tue, 09/02/2014 - 14:10
Teasing Out the Role of Big Structure in Context and Connections

The hackneyed phrase of “connect the dots” reflects our basic intuition that there is value in making connections amongst relevant data. But, what is this value? How might we quantify it? This topic, and a method and guidelines for doing so, are the subject of this article and the second and third parts to follow.

The reason it is important to quantify the value of connected information is that such an estimate helps to define what effort or cost we can justify in order to derive those connections. In Big Data, for example, we already know that 50% to 80% of the costs in assembling relevant datasets is due to data wrangling — the effort to extract, transform and clean the input data [1]. No where, however, do we know what it is worth to go to the next step of working to connect those data.

Quantifying this understanding will thus also help determine what the value is in developing Big Structure, the approach we have been most recently discussing for how to organize and connect Big Data. Big Structure sets the schematic and data relationships for how data from disparate sources can be connected together.

About five years ago I wrote my first articles on how we might approach the quantification of these information connections [2]. That first, cursory look was useful for bounding the problem, but no firm conclusions as to how to specifically quantify this value were proposed. Like other graphs or networks, the usefulness of the ‘network effect‘ to bound the question was clear. It has taken further research and experiences with actual linked datasets to point to how to resolve this quantification challenge.

Foundations in the Network Effect

The network effect was first realized in the early days of telephone networks, where the value of the system increased as a function of more users [3]. We have also long recognized a similar effect in connecting information together and the breaking down of information or ‘data silos‘. As the following diagram shows, unconnected data nodes or silos look like random particles caught in the chaos of Brownian motion:

Figure 1. ‘Network Effect’ for Connected Data

As initial connections get made, bits of structure begin to emerge. But, as connections are proliferated — analogous to the network effects of connected networks — coherence and more structure emerge.

This emergence of structure is particularly evident in physical networks, such as the growth of this hypothetical telecommunications network:

Figure 2. ‘Network Effect’ for Telecommunications Networks

This diagram, modified from Wikipedia to be a horizontal image, shows how two telephones can make only one connection, five can make 10 connections, and twelve can make 66 connections, etc. It is this very multiplier effect that has led to most of the thinking of how to quantify the network effect.

We can see an interesting parallel between telecommunications networks and knowledge graphs. In the telecommunication network, the addition of a new user (node) by definition brings with it connections. This is what is shown in Figure 2. But in information silos, the information is already there (nodes, or the left-side of Figure 1); what is missing are the connections (the right-side of Figure 1). By explicitly adding connections we can also create network effects, as others have noted [4].

However, once we understand these parallels, we must also recognize the differences. To properly estimate the network effects of knowledge graphs, we must be explicit about the similarities and differences with other (physical) networks [5].

Objectives for a Knowledge Graph Formulation

Since our objective is to quantify the “value” of a knowledge graph, we must first ask what is the basis of this value. In the best of all worlds, we would know the monetary worth of information, so we could justify what to spend in order to leverage it, which of course varies wildly across bases and sources. But we don’t. We do know, however, that a knowledge graph and the information it connects to constitutes a knowledge base. In the context of a knowledge base, the measure of value is the number of “correct” facts it contains. Therefore, we will use the number of connections in the graph (equivalent to the number of triple statements) as a proxy for value, representing the asserted “facts” of the graph.

We will also seek measures of graph distance and connectedness to capture the network-like qualities of the knowledge base. The characteristics of the graph itself should be the input base upon which to estimate value.

Alternative Estimates of the Network Effect

The earliest effort to estimate the value of physical networks was Sarnoff’s law, developed by David Sarnoff, for many years the leader of the Radio Corporation of America (RCA). He posited that the value of a broadcast network was directly proportional to its number of viewers (n). However, the problem with this formulation is that a broadcast network is only one way, from broadcaster to user. What of networks where there is interaction or two-way linkages? The benefits of such networks must surely be more than linear.

Once we get into interaction effects we get into multipliers. And the proper nature of those multipliers must come from the nature and extent of those interactions, as well as perhaps the nature of the network itself. I discuss below some of the more prominent candidates that have been put forward for estimating the network effect, or the value of networks.

Metcalfe’s Law

Metcalfe’s law was the first direct derivation from the telecommunications model. Robert Metcalfe formulated it about 1980 in relation to Ethernet and fax machines; the “law” was then named for Metcalfe and popularized by George Gilder in 1993. The actual algorithm proposed by Metcalfe calculates the number of unique connections in a network with n nodes to be n(n − 1)/2, which is proportional to n2. This makes Metcalfe’s law a quadratic growth equation.

The law is generally simplified [6] to state that the value of a telecommunications network is proportional to the square of the number of users of the system (n²):

Gilder’s popularization and the early growth of the Internet made the estimation of the benefits of network effects a very timely topic. For example, as a value measure, the network effect could be used to estimate the benefits for larger and larger numbers of users. Some have even blamed Metcalfe’s law for contributing to the creation (and then bursting) of the “dot-com bubble” of the late 1990s [7].

Metcalfe’s law clearly showed that interaction effects between nodes could generate multipliers that scaled rapidly with increasing numbers of nodes (users).

Reed’s Law

From a different perspective and with a different take, David Reed came up with a multiplier formulation that is the largest presented — anywhere. Reed’s context is social groups, and from that perspective he can envision arbitrary sized groups forming amongst any and all participants (nodes). Because of this theoretical, global scope, justified through examples such as eBay and chat rooms, Reed specifically defined group-forming networks (GFNs), as the applicable scope [8]. The simplified formulation for Reed is:

In scope and context, Reed does not apply to knowledge graphs, and even in the areas of social groups, most researchers find the exponential implications of Reed’s law unsupportable [9]. The next group, for example, offers direct criticism.

Briscoe – Odlyzko – Tilly Formulation

Under the provocative title, “Metcalfe’s Law is Wrong,” Briscoe, Odlyzko, and Tilly challenged both the Metcalfe and Reed approaches in 2006 [10]. Using the proxy of Internet valuation, the authors were able to show how impractical the implications of either approach were at scale. Like the bet of rice (or wheat) doubling each of the 64 squares on a chessboard bankrupting the kingdom, the exponential implications of these two “laws” can be seen to (eventually) violate common sense.

The fundamental fallacy associated with both the Metcalfe and Reed approaches is that all potential links are of equal value [10]. But no where in the real world do we see this to be true. There must be some law of diminishing returns that must be applied to slow the unsustainable rates of exponential or (to a lesser extent) quadratic growth.

After much hand waving, the authors chose Zipf’s law as their basis for this diminishing return. The increasing “decay rate” with distance is a common distribution pattern for real-world datasets, which Zipf’s law specifically addresses, always showing power law distributions with long tails. To approximate this distribution they offered the simple n log (n) formulation of Zipf’s law [11].

This is a reasonable approximation, but one that is never related directly to the nature of graphs or networks. That is the source of the next layer of refinements.

VKG Formulation

I will discuss this algorithm, our recommended formulation, in Part II of this series.

A Big Data Example

In order to discuss further the question of value arising from network effects, we need a case study example. We also need to define “value”, which in this case study example, as also noted above, means the number of “facts” or assertions in our database [12]. This we call the Basic Facts (“assertions”) column.

For our basic “facts”, we consider a data series that is doubling in size for each step, eventually reaching a half billion records. Each record has four attributes or characterizations, leading to a total of more than 2 billion “facts” in the database. These are the first two columns in this table:

Records Basic Facts (“assertions”) Big Data Example 1 4 4 2 8 8 4 16 16 8 32 32 16 64 64 32 128 128 64 256 256 128 512 512 256 1,024 1,024 512 2,048 2,048 1,024 4,096 4,100 2,048 8,192 8,200 4,096 16,384 16,400 8,192 32,768 32,800 16,384 65,536 65,600 32,768 131,072 131,200 65,536 262,144 262,404 131,072 524,288 524,812 262,144 1,048,576 1,049,624 524,288 2,097,152 2,099,248 1,048,576 4,194,304 4,198,496 2,097,152 8,388,608 8,396,996 4,194,304 16,777,216 16,793,992 8,388,608 33,554,432 33,587,984 16,777,216 67,108,864 67,175,972 33,554,432 134,217,728 134,351,944 67,108,864 268,435,456 268,703,888 134,217,728 536,870,912 537,407,780 268,435,456 1,073,741,824 1,074,815,564 536,870,912 2,147,483,648 2,149,631,128 Table 1. Basic Facts Connections with a Big Data Example

At this point, we have no connections between records. Each record has four attributes each, in isolation. This basis is akin to the unconnected dots on the left side of Figure 1 above.

For our Big Data Example, we will posit a record matching procedure as our first task for a new Big Data initiative. The assumption is that across all records, one-in-10000 matches another record. This results in the number of assertions (“facts”) shown in the third column in the table above. The posited Big Data initiative results in a 0.10% increase in “facts”, irrespective of record scale, once the matching threshold is reached. This result is not terribly impressive, but is perhaps not too unrelated from a first foray into a Big Data project.

Note that the following charts and analyses (including in the next part tomorrow) use as their “Basic Facts” the number of “assertions”, or the middle column in the table above. Though Big Data may represent an initial 0.10% improvement over this, that is immaterial to what our Big Structure viewpoints will provide. So, our “Basic Facts” will be unconnected records.

Applying Network Effects to the Basic Facts

We can now apply our various network effect estimators to this base case. And, because of the fast-compounding nature of both the Reed and Metcalfe approaches, we need to plot this out on logarithmic scale [13] (click to enlarge):

Figure 3. Knowledge Network Estimates

On a logarithmic scale, the O-T (Briscoe-Odlyzko-Tilly) and VKG formulations appear only marginally better than the Basic Facts base case, but that is only due to the swamping effects of the unrealistic growth multipliers. We’ll get into this more tomorrow.

Preview of Next Part

The next part forthcoming tomorrow will use this foundation to describe the VKG algorithm, and some of implications of its characteristics, all in the context of knowledge networks or graphs.

[1] “Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets,” is a quote from Steve Lohr, 2014, “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights,” August 17, 2014, New York Times, see http://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html. Also, as another example of the common 80% estimate for data preparation costs, see http://radar.oreilly.com/2013/09/data-analysis-just-one-component-of-the-data-science-workflow.html. [2] These two articles were, M.K. Bergman, 2009. Structure the World, in AI3:::Adaptive Information blog, August 3, 2009, and M.K. Bergman, 2009, The Law of Linked Data, AI3:::Adaptive Information blog, October 11, 2009. The same concerns I had at that time in the current state of analysis was captured by Eric Hellman, 2009. Normal and Inverse Network Effects for Linked Data, published in his blog, October 15, 2009; see http://go-to-hellman.blogspot.com/2009/10/normal-and-inverse-network-effects-for.html. [3] These network effect benefits were reportedly a major driver of Theodore Newton Vail‘s efforts to consolidate the thousands of initial telephone networks in the United States under the banner of the American Telephone & Telegraph (Ma Bell) company. [4] See, for example, James Hendler and Jennifer Golbeck, 2008. Metcalfe’s Law, Web 2.0, and the Semantic Web, in Web Semantics: Science, Services and Agents on the World Wide Web 6(1): 14-20; see http://www.cs.umd.edu/~golbeck/downloads/Web20-SW-JWS-webVersion.pdf. [5] Babak Hodjat and Adam Cheyer, 2003. Evolution of the Laws that Deal with the Utilization of Information Networks, in Masoud Nikravesh, Lotfi A. Zadeh and Janusz Kacprzyk, eds., Studies in Fuzziness and Soft Computing, Vol 164/2005, pp. 427-438, Springer, Berlin. See http://www.adam.cheyer.com/papers/KnowledgeNetworks_Formatted.pdf. [6] For a well-connected network, every node (n) connects to every other node (n-1), which gives us n*(n-1) or (n2 – n). Working this out, two nodes have two connections (2*2 – 2), three nodes have six connections (3*3 – 3) and the expression converges on the square of ‘n’ for larger values of ‘n’, e.g., (100*100 – 100) is 99% of (100*100). This convergence at larger number is the basis for the exponential simplification, 2n. Most of the other ‘laws’ stated herein are simplifications in a similar manner. [7] See, for example, Sara F. Peralta, 2011. Moore’s Law, Metcalfe’s Law, and the Dot Com Bubble, November 27, 2011, see https://storify.com/sarafperalta/moore-s-law-metcalfe-s-law-bubble. Also see [10]. [8] David P. Reed, 1999. That Sneaky Exponential—Beyond Metcalfe’s Law to the Power of Community Building, August 27, 1999; online at http://www.reed.com/dpr/locus/gfn/reedslaw.html. For original version, see http://contextmag.com/archives/199903/digitalstrategyreedslaw.asp. Like Metcalfe, at smaller numbers the actual formula is 2n -n – 1, which rapidly converges to 2n. [9] However, one group has published an alternative formulation consistent with the Reed approach; see Kalevi Kilkki, and Matti Kalervo, 2004. KK-law for Group Forming Services, in XVth International Symposium on Services and Local Access, Edinburgh, March 2004. See http://kotisivukone.fi/files/50ajatelmaa.ajatukset.fi/tiedostot/Others/kilkki_kk-law.pdf. [10] Bob Briscoe, Andrew Odlyzko, and Benjamin Tilly, 2006. “Metcalfe’s Law is Wrong,” in IEEE Spectrum, July 2006. A copy may be viewed at http://www.cse.unr.edu/~yuksem/teaching/nae/reading/2006-briscoe-metcalfes.pdf. Odlyzko, and Tilly had published an earlier version, (sometimes the approach is shown as O-T in addition to B-O-T), and the basic form of the algorithm appears in a single Odlyzko paper. [11] See, for example, https://www.princeton.edu/~achaney/tmve/wiki100k/docs/Zipf_s_law.html. [12] In specific terms, each “fact” in our knowledge base is an assertion, represented as an RDF triple statement. Because some of these assertions may not, in fact, be true, the use of “fact” does not imply universal truthfulness. Rather, an assertion that passes current tests for logic, coherency, consistency or completeness is what is retained, even though its truthfulness is not certain. Therefore, separate adjustment factors (parameters) need to be applied to address accuracy tests. [13] We adjusted the scale further to reduce the exponential absurdity of the Reed approach by manually shifting the scale downward. As a result, the Reed approach exits the chart rather quickly, heading straight up.
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