Posted:July 23, 2014

Light and Dark Structure of Universe, @NYT, see http://vimeo.com/100907866Envisioning A New Adaptive Infrastructure for Data Interoperability

In Part I of this two-part series, Fred Giasson and I looked back over a decade of working within the semantic Web and found it partially successful but really the wrong question moving forward. The inadequacies of the semantic Web to date reside in its lack of attention to practical data interoperability across organizational or community boundaries. An emphasis on linked data has created an illusion that questions of data integration are being effectively addressed. They are not.

Linked data is hard to publish and not the only useful form for consuming data; linked data quality is often unreliable; the linking predicates for relating disparate data sources to one another may be inadequate or wrong; and, there are no reference groundings for relating data values across datasets. Neither the semantic Web nor linked data has developed the practices, tooling or experience to actually interoperate data across the Web. These criticisms are not meant to condemn linked data — it is, after all, the early years. Where it is compliant and from authoritative information sources, linked data can be a gold standard in data publishing. But, linked data is neither necessary nor essential, and may even be a diversion if it sucks the air from the room for what is more broadly useful.

This table summarizes the state-of-art in the semantic Web for frameworks and guidance in how to interoperate data:

Category Related Terms Status in the Semantic Web Notes
Classes sets, concepts, topics, types, kinds Mature, but broader scope coverage desirable; equivalent linkages between datasets often mis-applied; more realistic proximate linkages in flux, with no bases to reason over them [1]
Instances individuals, entities, members, records, things Current basis for linked data; many linkage properties mis-applied [2]
Relation Properties relations, predicates Equivalent linkages between datasets often mis-applied; more realistic proximate linkages in flux, with no bases to reason over them. [3]
Descriptive Properties attributes, descriptors Save for a couple of minor exceptions, no basis for mapping attributes across datasets [4]
Values data Basic QUDT ontologies could contribute here [5]

We can relate the standard subjectpredicateobject triple statement in RDF to this table, using the Category column. Classes and Instances relate to the subjects, Relation and Descriptive Properties relate to the predicate, and Values relate to the object [6] in an RDF triple. The concepts and class schema of different information sources (their “aboutness”) can reasonably be made to interoperate. In terms of the description logics that underly the logic bases of W3C ontologies, the focus and early accomplishments of the semantic Web have been on this “terminological box” or T-Box [7]. Tooling to make the mappings more productive and means to test the coherence and completeness of the results still remain as priority efforts, but the conceptual basis and best practices have progressed pretty well.

In contrast, nearly lacking in focus and tooling has been the flip side of that description logics coin: the A-Box [7], or assertional and instance (data) level of the equation. Both the T-Box and A-Box are necessary to provide a knowledge base. Today, there are virtually no vocabularies, no tooling, no history, no best practices and no “grounding” for actual A-Box data integration within the semantic Web. Without such guidance, the semantic Web is silent on the questions of data interoperability. As David Karger explained in his keynote address at ISWC in 2013 [8], “we’ve got our heads in the clouds while people are stuck in the dirt.”

Yet these are not fatal flaws of the semantic Web, nor are they permanent. Careful inspection of current circumstances, combined with purposeful action, suggests:

  1. Data integration can be solved
  2. Leveraging background knowledge is a key enabler
  3. Interoperability requires reference structures, what we are calling Big Structure.

The Prism of Data Interoperability

Why do we keep pointing to the question of data interoperability? Consider these facts:

  • 80% of all available information is in text or documents (unstructured)
  • 40% of standard IT project expenses are devoted to data integration in one form or another, due to the manual effort needed for data migration and mapping
  • Information volumes are now doubling in fewer than two years
  • Other trends including smartphones and sensors are further accelerating information growth
  • Effective business intelligence requires the use of quality, integrated data.

The abiding, costly, frustrating and energy-sucking demands of data integration have been a constant within enterprises for more than three decades. The same challenges reside for the Web. The Internet of Things will further demand better interoperability frameworks and guidelines. Current data integration tooling relies little upon semantics and no leading alternative is based principally around semantic approaches [9].

The data integration market is considered to include enterprise data integration and extract, transform and load (ETL) vendors. Gartner estimates tool sales for this market to be about $2 billion annually, with a growth rate faster than most IT areas [10]. But data integration also touches upon broader areas such as enterprise application integration (EAI), federated search and query, and master data management (MDM), among others. Given that data integration is also 40% of standard IT project costs, new approaches are needed to finally unblock the costly logjam of enterprise information integration. Most analysts see firms that are actively pursuing data integration innovations as forward-thinking and more competitive.

Data integration is combining information from multiple sources and providing users a uniform view of it. Data interoperability is being able to exchange and work upon (inter-operate) information across system and organizational boundaries. The ability to integrate data precedes the ability to interoperate it. For example, I may have three datasets of mammals that I want to consolidate and describe in similar terms with common units of measurement. That is an example of data integration. I may then want to relate this mammal knowledge base with a more general perspective of the animal kingdom. That is an example of data interoperability. Data integration usually occurs within a single organization or enterprise or institutional offering (as would be, say, Wikipedia). Data interoperability additionally needs to define meanings and communicate them in common ways across organizational, domain or community boundaries.

These are natural applications for the semantic Web. Why, then, has there not been more practical use of the semantic Web for these purposes?

That is an interesting question that we only partially addressed in Part I of this series. All aspects of data have semantics: what the data is about, what is its context, how it relates to other data, and what its values are and what they mean. The semantic Web is closely allied with natural language processing, an essential for bringing the 80% of unstructured data into the equation. Semantic Web ontologies are useful structures for how to relate real-world data into common, reference forms. The open world logic of the semantic Web is the right perspective for knowledge functions under the real-world conditions of constantly expanding information and understandings.

While these requirements suggest an integral role for the semantic Web, it is also clear that the semantic Web has not yet made these contributions. One explanation may be that semantic Web advocates, let alone the linked data tribe, have not seen data integration — as traditionally defined — as their central remit. Another possibility is that trying to solve data interoperability through the primary lens of the semantic Web is the wrong focus. In any case, meeting the challenge of data interoperability clearly requires a much broader context.

Embedding Data Interoperability Into a Broader Context

The semantic Web, in our view, is properly understood as a sub-domain of artificial intelligence. Semantic technologies mesh smoothly with natural language tasks and objectives.  But, as we noted in a recent review article, artificial intelligence is itself undergoing a renaissance [11]. These advances are coming about because of the use of knowledge-based AI (KBAI), which combines knowledge bases with machine learning and other AI approaches. Natural language and spoken interfaces combined with background knowledge and a few machine-language utilities are what underlie Apple’s Siri, for example.

The realization that the semantic Web is useful but insufficient and that AI is benefitting from the leveraging of background knowledge and knowledge bases caused us to “decompose” the data-interoperability information space. Because artificial intelligence is a key player here, we also wanted to capture all of the main sub-domains of AI and their relationships to one another:

Artificial Intelligence Domains

Artificial Intelligence Domains

Two core observations emerge from standing back and looking at these questions. First, many of AI’s main sub-domains have a role to play with respect to data integration and interoperability:

AI and Data Interoperability

AI Domains Related to Data Interoperability

This places semantic Web technologies as a co-participant with natural language processing, knowledge mining, pattern recognizers, KR languages, reasoners, and machine learning as domains related to data interoperability.

And, second, generalizing the understanding of knowledge bases and other guiding structures in this space, such as ontologies, highlights the potential importance of Big Structure. Virtually every one of the domains displayed above would be aided by leveraging background knowledge.

Grounding Data Interoperability in Big Structure

As our previous AI review showed [11], reference knowledge bases — Wikipedia in the forefront — have been a tremendous boon to moving forward on many AI challenges. Our own experience with UMBEL has also shown how reference ontologies can help align and provide common grounding for mapping different information domains into one another [12]. Vetted, gold-standard reference structures provide a fixity of coherent touchpoints for orienting different concepts and domains (and, we believe, data) to one another.

In the data integration context, master data models (and management, or MDM) attempt to provide common reference terms and objects to aid the integration effort. Like other areas in conventional data integration, very few examples of MDM tools based on semantic technologies exist.

This use of reference structures and the importance of knowledge bases to help solve hard computational tasks suggests there may be a general principle at work. If ontologies can help orient domain concepts, why can’t they also be used to orient instance data and their attributes? In fact, must these structures always be ontologies? Are not other common reference structures such as taxonomies, vocabularies, reference entity sets, or other typologies potentially useful to data integration?

By standing back in this manner and asking these broader questions we can see a host of structures like reference concepts, reference attributes, reference places, reference identifiers, and the like, playing the roles of providing common groundings for integration and interoperation. Through the AI experience, we can also see that subsequent use of these reference structures — be they full knowledge bases or more limited structures like taxonomies or typologies — can further improve information extraction and organization. The virtuous circle of knowledge structures improving AI algorithms, which can then further improve the knowledge structures, has been a real Aha! moment for the artificial intelligence community. We should see rapid iterations of this virtuous circle in the months to come.

These perspectives can help lead to purposeful designs and approaches for attacking such next-generation problems as data interoperability. The semantic Web can not solve this alone because additional AI capabilities need to be brought to bear. Conventional data integration approaches that lack semantic Big Structure groundings — let alone the use of AI techniques — have years of history of high cost and disappointing results. No conventional enterprise knowledge management problem appears sheltered from this whirlwind of knowledge-backed AI.

At Structured Dynamics, Fred Giasson and I have been discussing “Big Structure” for some time. However, it was only in researching this article that I came across the first public use of this phrase in the context of AI and big data. In May, Dr. Jiawei Han, a leading researcher in data mining, gave a lecture at Yahoo! Labs entitled, Big Data Needs Big Structure. In it, he defines “Big Structure as a type information network.” The correlation with ontologies and knowledge structures is obvious.

An Emerging Development Agenda

The intellectual foundations already exist to move aggressively on a focused development agenda to improve the infrastructure of data interoperability. This emerging agenda needs to look to new refererence structures, better tooling, the use of functional languages and practices, and user interfaces and workflows that improve the mappings that are the heart of interoperability.

Big Structure, such as UMBEL for referencing what data is about, is the present exemplar for going forward. Excellent reference and domain ontologies for common domains already exist. Mapping predicates have been developed for these purposes. Though creation of the maps is still laborious, tooling improvements (see below) should speed up that process as well.

What is next needed are reference structures to help guide attributes mappings, data value mappings, and transformations into usable common attribute quantities and types. I will discuss in a later post our more detailed thoughts of what a reference gold-standard attribute ontology should look like. This new Big Structure should also be helpful in guiding conversion, transformation and “lifting” utilities that may be used to bring attribute values from heterogeneous sources into a common basis. As mappings are completed, these too can become standard references as the bootstrapping continues.

Mappings for data integration across the scales, scope and growth of data volumes on the Web and within enterprises can no longer be handled manually. Semi-automated tooling must be developed and refined that operates over large volumes with acceptable performance. Constant efforts to reduce the data volumes requiring manual curation are essential; AI approaches should be incorporated into the virtuous iterations to reduce these efforts. Meanwhile, attentiveness to productive user interfaces and efficient workflows are also essential to improve throughput.

Further, by working off of standards-based Big Structures, this tooling can be made more-or-less generic, with ready application to different domains and different data. Because this tooling will often work in enterprises behind firewalls, standard enterprise capabilities (security, access, preservation, availability) should also be added to this infrastructure.

These Big Structures and tools should themselves be created and maintained via functional programming languages and DSLs specifically geared to the circumstances at hand. We want languages suited to RDF and AI purposes with superior performance across large mapped datasets and unstructured text. But we also want languages that are easier to use and maintain by knowledge workers themselves. Partitioning strategies may also need to be employed to ensure acceptable real-time feedback to users responsible for data integration mappings.

A New Adaptive Infrastructure for Data Interoperability

Structured Dynamics’ review exercise, now documented in this two-part series, affirms the semantic Web needs to become re-embedded in artificial intelligence, backed by knowledge bases, which are themselves creatures of the semantic Web. Coupling artificial intelligence with knowledge bases will do much to improve the most labor-intensive stumbling blocks in the data integration workflow: mappings and transformations. Through a purposeful approach of developing reference structures for attributes and data values, we will begin to see marked improvements in the efficiency and lower costs of data integration. In turn, what is learned by using these approaches for mastering MDM will teach the semantic Web much.

An approach using semantic technologies and artificial intelligence tools will begin to solve the data integration puzzle. By leveraging background knowledge, we will begin to extend into data interoperability. Purposeful attention to tooling and workflows geared to improve the mapping speed and efficiency by users will enable us to increase the stable of reference structures — that is, Big Structure — available for the next integration challenges. As this roster of Big Structures increases, they can be shared, allowing more generic issues of data integration to be overcome, freeing domains and enterprises to target what is unique.

Achieving this vision will not occur overnight. But, based on a decade of semantic Web experience and the insights being gained from today’s knowledge-based AI advances, the way forward looks pretty clear. We are entering a fundamental new era of knowledge-based computation. We welcome challenging case examples that will help us move this vision forward.

NOTE: This Part II concludes the series with Part I, A Decade in the Trenches of the Semantic Web

[1] Using semantic ontologies can and has worked well for many domains and applications, such as the biomedical OBO ontologies, IBM’s Watson, Google’s Knowledge Graph, and hundreds in more specific domains. Combined with concept reference structures like UMBEL, both building blocks and exemplars exist for how to interoperate across what different domains are about.
[2] For examples of issues, see M. K. Bergman, 2009. When Linked Data Rules Fail, AI3:::Adaptive Information blog, November 16, 2009.
[3] Some of these options are overviewed by M. K. Bergman, 2010. The Nature of Connectedness on the Web, AI3:::Adaptive Information blog, November 22, 2010.
[4] See the thread on the W3C semantic web mailing list beginning at http://lists.w3.org/Archives/Public/semantic-web/2014Jul/0129.html.
[6] The object may also refer to another class or instance, in which case the relation property takes the form of an ObjectProperty and the “value” is the URI referring to that object.
[7] See, for example, M. K. Bergman, 2009. Making Linked Data Reasonable Using Description Logics, Part 2, AI3:::Adaptive Information blog, February 15, 2009.
[9] Info-Tech Research Group, 2011. Vendor Landscape Plus: Data Integration Tools, 72 pp.
[10] Gartner estimates that the data integration tool market was slightly over $2 billion at the end of 2012, an increase of 7.4% from 2011. This market is seeing an above-average growth rate of the overall enterprise software market, as data integration continues to be considered a strategic priority by organizations. See Eric Thoo, Ted Friedman, Mark A. Beyer, 2013. Magic Quadrant for Data Integration Tools, research Report G00248961 from Gartner, Inc., 17 July 2013; see: http://www.gartner.com/technology/reprints.do?id=1-1HBEFSF&ct=130717&st=sb
[11] See M. K. Bergman, 2014. Spring Dawns on Artificial Intelligence, AI3:::Adaptive Information blog, June 2, 2014.
[12] See M. K. Bergman, 2011. In Search of ‘Gold Standards’ for the Semantic Web, AI3:::Adaptive Information blog, February 28, 2011.
Posted:July 16, 2014

Battle of Niemen, WWI, photo from WikimediaAre We Losing the War? Was it Even the Right One?

Cinemaphiles will readily recognize Akira Kurosawa‘s Rashomon film of 1951. and in the 1960s one of the most popular book series was Lawrence Durrell‘s The Alexandria Quartet. Both, each in its own way, tried to get at the question of what is truth by telling the same story from the perspective of different protagonists. Whether you saw this movie or read these books you know the punchline: the truth was very different depending on the point of view and experience — including self-interest and delusion — of each protagonist. All of us recognize this phenomenon of the blind men’s view of the elephant.

I have been making my living and working full time on the semantic Web and semantic technologies now for a full decade. So has my partner at Structured Dynamics, Fred Giasson. Others have certainly worked longer in this field. The original semantic Web article appeared in Scientific American in 2000 [1], and the foundational Resource Description Framework data model dates from 1999. Fred and I have our own views of what has gone on in the trenches of the semantic Web over this period. We thought a decade was a good point to look back, share what we’ve experienced, and discover where to point our next offensive thrusts.

What Has Gone Well?

The vision of the semantic Web in the Scientific American article painted a picture of globally interconnected data leveraged by agents or bots designed to make our lives easier and more automated. However, by the time that I got directly involved, nearly five years after standards first started to be published, Tim Berners-Lee and many leading proponents of RDF were beginning to shift focus to linked data. The agents, and automation, and ontologies of the initial vision were being downplayed in favor of effective means to publish and consume data based on RDF. In many ways, linked data resembled a re-branding.

This break had been coming for a while, memorably captured by a 2008 ISWC session led by Peter F. Patel-Schneider [2]. This internal division of viewpoint likely caused effort to be split that would have been better spent in proselytizing and improving tools. It also diverted somewhat into internal squabbles. While many others have pointed to a tactical mistake of using an XML serialization for early versions of RDF as a key factor is slowing initial adoption, a factor I agree was at play, my own suspicion is that the philosophical split taking place in the community was the heavier burden.

Whatever the cause, many of the hopes of the heady days of the initial vision have not been obtained over the past fifteen years, though there have been notable successes.

The biomedical community has been the shining exemplar for data interoperability across an entire discipline, with earth sciences, ecology and other science-based domains also showing interoperability success [3]. Families of ontologies accompanied by tooling and best practices have characterized many of these efforts. Sadly, though, most other domains have not followed suit, and commercial interoperability is nearly non-existent.

Most all of the remaining success has resided in single-institution data integration and knowledge representation initiatives. IBM’s Watson and Apple’s Siri are two amazing capabilities run and managed by single institutions, as is Google’s Knowledge Graph. Also, some individual commercial and government enterprises, willing to pay support to semantic technology experts, have shown success in data integration, using RDF, SKOS and OWL.

We have seen the close kinship between natural language, text, and Q & A with the semantic Web, also demonstrated by Siri and more recent offshoots. We have seen a trend toward pairing great-performing open source text engines, notably Solr, with RDF and triple stores. Recommendation systems have shown some success. Linked data publishing has also had some notable examples, including the first of the lot, DBpedia, with certain institutional publishers (such as the Library of Congress, Eurostat, The Getty, Europeana, OpenGLAM [galleries, archives, libraries, and museums]) showing leadership and the commitment of significant vocabularies to linked data form.

On the standards front, early experience led to new and better versions of the SPARQL query language (SPARQL 1.1 was greatly improved in the last decade and appears to be one capability that sells triple stores), RDF 1.1 and OWL 2. Certain open source tools have become prominent, including Protégé, Virtuoso (open source) and Jena (among unnamed others, of course). At least in the early part of this history, tool development was rapid and flourishing, though the innovation pace has dropped substantially according to my tracking database Sweet Tools.

What Has Disappointed?

My biggest disappointments have been, first, the complete lack of distributed data interoperability, and, second, the lack or inability of commercial enterprises to embrace and adopt semantic technologies on their own. The near absence of discussion about instance records and their attributes helps frame the current maturity of the semantic Web. Namely, it has yet to crack the real nuts of data integration and interoperability across organizations. Again, with the exception of the biomedical community, neither in the linked data realm nor in the broader semantic Web, can we point to information based on semantic Web principles being widely shared between systems and organizations.

Some in the linked data community have explicitly acknowledged this. The abstract for the upcoming COLD 2014 workshop, for example, states [4]:

. . . applications that consume Linked Data are not yet widespread. Reasons may include a lack of suitable methods for a number of open problems, including the seamless integration of Linked Data from multiple sources, dynamic discovery of available data and data sources, provenance and information quality assessment, application development environments, and appropriate end user interfaces.

We have written about many issues with linked data, ranging from the use of improper mapping predicates; to the difficulty in publishing; and to dereferencing URIs on the Web since they are sparse and not always properly implemented [5]. But ultimately, most linked data is just instance data that can be represented in simpler attribute-value form. By shunning a knowledge representation language (namely, OWL) at the processing end, we have put too much burden on what are really just instance records. Linked data does not get the balance of labor right. It ignores the reality that data consumers want actionable information over being able to click from data item to data item, with overall quality reduced to the lowest common denominator. If a publisher has the interest and capability to publish quality linked data, great! It should become part of the data ingest pool and the data becomes easy to consume. But to insist on linked data across the board creates unnecessary barriers. Linked data growth has not nearly kept pace with broader structured data growth on the Web [6].

At the enterprise level, the semantic technology stack is hard to grasp and understand for newcomers. RDF and OWL awareness and understanding are nearly nil in companies without prior semantic Web experience, or 99.9% of all companies. This is not a failure of the enterprises; it is the failure of us, the advocates and suppliers. While we (Structured Dynamics) have developed and continue to refine the turnkey Open Semantic Framework stack, and have spent more efforts than most in documenting and explicating its use, the systems are still too complicated. We combine complicated content management systems as user front-ends to a complicated semantic technology stack that needs to be driven by a complicated (to develop) ontology. And we think we are doing some of the best technology transfer around!

Moreover, while these systems are good at integrating concepts and schema, they are virtually silent on the question of actual data integration. It is shocking to say, but the semantic Web has no vocabularies or tools sufficient to enable data items for the same entity from two different datasets to be combined or reconciled [7]. These issues can be solved within the individual enterprise, but again the system breaks when distributed interoperability is the desire. General Web-based inconsistencies, such as in HTML coding or mime types, impose hurdles on distributed interoperability. These are some of the reasons why we see the successes in the context (generally) of single institutions, as opposed to anything that is truly yet Web-wide.

These points, as is often the case with software-oriented technologies, come down to a disappointing state of tooling. Markets drive developer interest, and market share has been disappointing; thus, fewer tools. Tool interest comes from commercial engagements, and not generally grants, the major source of semantic Web funding, particularly in the European Union. Pragmatic tools that solve real problems in user adoption are rarely a sufficient basis for getting a Ph.D.

The weaknesses in tooling extend from basic installation, to configuration, unit and integrated tests, data conversion and lifting, and, especially, all things ontology. Weaknesses in ontology tooling include (critically) mapping, consistency and coherency checking, authoring, managing, version control, re-factoring, optimization, and workflows. All of these issues are solvable; they are standard software challenges. But it is hard to conquer markets largely with the wrong army pursuing the wrong objectives in response to the wrong incentives.

Yet, despite the weaknesses in tooling, we believe we have been fairly effective in transferring technology to our clients. It takes more documentation and more training and, often, accompanying tool development or improvement in the workflow areas critical to the project. But clients need to be told this as well. In these still early stages, successful clients are going to have to expend more staff effort. With reasonable commitment, it is demonstrable that an enterprise can take over and manage a large-scale semantic engagement on its own. Still, for semantic technologies to have greater market penetration, it will be necessary to lower those commitments.

How Has the Environment Changed?

Of course, over the period of this history, the environment as a whole has changed markedly. The Web today is almost unrecognizable from the Web of 15 years ago. If one assumes that Web technologies tend to have a five year or so period of turnover, we have gone through at least two to three generations of change on the Web since the initial vision for the semantic Web.

The most systemic changes in this period have been cloud computing and the adoption of the smartphone. These, plus the network of workstations approach to data centers, have radically changed what is desirable in a large-scale, distributed architecture. APIs have become RESTful and database infrastructures have become flatter and more distributed. These architectures and their supporting infrastructure — such as virtual servers, MapReduce variants, and many applications — have in turn opened the door to performant management of large volumes of flat (key-value or graph) data, or big data.

On the Web side, JavaScript, just a few years older than the semantic Web, is now dominant in Web pages and taking on server-side roles (such as through Node.js). In turn, JSON has now grown in popularity as a form of data representation and transfer and is being adopted to the semantic Web (along with codifying CSV). Mobile, too, affects the Web side because of the need for multiple-platform deployments, touchscreen use, and different user interface paradigms and layout designs. The app ecosystem around smartphones has become a huge source for change and innovation.

Extremely germane to the semantic Web — indeed, overall, for artificial intelligence — has been the occurrence of knowledge-based AI (KBAI). The marrying of electronic Web knowledge bases — such as Wikipedia or internal ones like the Google search index or its Knowledge Graph — with improvements in machine-learning algorithms is systematically mowing down what used to be called the Grand Challenges of computing. Sensors are also now entering the picture, from our phones to our homes and our cars, that exposes the higher-order requirement for data integration combined with semantics. NLP kits have improved in terms of accuracy and execution speed; many semantic tasks such as tagging or categorizing or questioning already perform at acceptable levels for most projects.

On the tooling side, nearly all building blocks for what needs to be done next are available in open source, with some platform areas quite functional (including OSF, of course). We have also been successful in finding clients that agree to open source the development work we do for them, since they are benefiting from the open source development that went on before them.

What Did We Set Out to Achieve?

When Structured Dynamics entered the picture, there were already many tools available and core languages had been released. Our view of the world at that time led us to adopt two priorities for what we thought might be a five year or so plan. We have achieved the objectives we set for ourselves then, though it has taken us a couple of years longer to realize.

One priority was to develop a reference structure for concepts to serve as a “grounding” basis for relating datasets, vocabularies, schema, taxonomies, or ontologies. We achieved this with our first commercial release (v 1.00) of UMBEL in February 2011. Subsequent to that we have progressed to v 1.05. In the coming months we will see two further major updates that have been under active effort for about eight months.

The other priority was to create a turnkey foundation for a semantic enterprise. This, too, has been achieved, with many more releases. The Open Semantic Framework (OSF) is now in version 3.00, backed by a 500-article training documentation and technical wiki. Support tooling now includes automated installation, testing, and data transfer and synchronization.

Because our corporate objectives were largely achieved it was time to look at lessons learned and set new directions. This article, in part, is a result of that process.

How Did Our Priorities Evolve Over the Decade?

I thought it would be helpful to use the content of this AI3 blog to track how concerns and priorities changed for me and Structured Dynamics over this history. Since I started my blog quite soon after my entry into the semantic Web, the record of my perspectives was conterminous and rather complete.

The fifty articles below trace my evolution in knowledge and skills, as well as a progression from structured data to the semantic Web. These 50 articles represent about 11% of all articles in my chronological archive; they were selected as being the most germane to the question of evolution of the semantic Web.

After early ramp up, most of the formative discussion below occurred in the early years. Posts have declined most recently as implementation has taken over. Note most of the links below have  PDFs available from their main pages.

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

The early years of this history were concentrated on gathering background information and getting educated. The release of DBpedia in 2007 showed how knowledge bases would become essential to the semantic Web. We also identified that a lack of shared reference concepts was making it difficult to “ground” different semantic Web datasets or schema to one another. Another key theme was the diversity of native data structures on the Web, but also how all of them could be readily represented in RDF.

By 2008 we began to study the logical underpinnings to the semantic Web as we were coming to understand how it should be practiced. We also began studying Web-oriented architectures as key design guidance going forward. These themes continued into 2009, though now informed by clients and applications, which was expanding our understanding of requirements (and, sometimes, shortcomings) in the enterprise marketplace. The importance of an open world approach to the basic open nature of knowledge management was cementing a clarity of the role and fit of semantic solutions in the overall informaton space. The general community shift to linked data was beginning to surface worries.

2010 marked a shift for us to become more of a popularizer of semantic technologies in the enterprise, useful to attract and inform prospects. The central role of ontologies as the guiding structures (either as codified knowledge structures or as instruction sets for the platform) for OSF opened realizations that generic functional software could be designed that can be re-used in most any knowledge domain by simply changing the data and ontologies guiding them. This increased our efforts in ontology tooling and training, now geared more to the knowledge worker.  The importance of groundings for aligning schema and data caused us to work hard on UMBEL in 2011 to get it to a commercial release state.

All of these efforts were converging on design thoughts about the nature of information and how it is signified and communicated. The bases of an overall philosophy regarding our work emerged around the teachings of Charles S Peirce and Claude Shannon. Semantics and groundings were clearly essential to convey accurate messages. Simple forms, so long as they are correct, are always preferred over complex ones because message transmittal is more efficient and less subject to losses (inaccuracies). How these structures could be represented in graphs affirmed the structural correctness of the design approach. The now obvious re-awakening of artificial intelligence helps to put the semantic Web in context: a key subpart, but still a subset, of artificial intelligence. The percentage of formative articles directly related over these last couple of years to the semantic Web drops much, as the emphasis continues to shift to tech transfer.

What Else Did We Learn?

Not all lessons learned warranted an article on their own. So, we have also reflected on what other lessons we learned over this decade. The overall theme is: Simpler is better.

Distributed data interoperability across the Web is a fundamental weakness. There are no magic tricks to integrate data. Data mapping and integration will always require massaging. Each data integration activity needs its own solution. However, it can greatly be helped with ontologies and with better tooling.

In keeping with the lesson of grounding, a reference ontology for attributes is missing. It is needed as a bridge across disparate datasets describing similar entities or with different attributes for the same entities. It is also a means to reduce the pairwise combinatorial issue of integrating multiple datasets. And, whatever is done in the data integration area, an open world approach will be essential given the nature of knowledge information.

There is good design and best practice for distributed architectures. The larger these installations become, the more important it is to use a lightweight, loosely-coupled design. RESTful Web services and their interfaces are key. Simpler services with fewer functions can be designed to complement one another and increase throughput effectiveness.

Functional programming languages align well with the data and schema in knowledge management functions. Ontologies, as structures, also fit well with functional languages. The ability to create DSLs should continue to improve bringing the knowledge management function directly into the hands of its users, the knowledge workers.

In a broader sense, alluded to above, the semantic Web is but a set of concepts. There are multiple ways to use it. It can be leveraged without requiring “core” semantic Web tools such a triple stores. Solr can act as a semantic store because semantics, NLP and search are naturally married. But, the semantic Web, in turn, needs to become re-embedded in artificial intelligence, now backed by knowledge bases, which are themselves creatures of the semantic Web.

Design needs to move away from linked data or the semantic Web as the goals. The building blocks are there, though perhaps not yet combined or expressed well. The real improvements now to the overall knowledge function will result from knowledge bases, artificial intelligence, and the semantic Web working together. That is the next frontier.

Overall, we perhaps have been in the wrong war for the wrong reasons. Linked data is certainly not an end and mostly appears to represent work, rather than innovation. The semantic Web is no longer the right war, either, because improvements there will not come so much from arguing semantic languages and paradigms. Learning how to master distributed data integration will teach the semantic Web much, and coupling artificial intelligence with knowledge bases will do much to improve the most labor-intensive stumbling blocks in the knowledge management workflow: mappings and transformations. Further, these same bases will extend the reach into analytical and statistical realms.

The semantic Web has always been an infrastructure play to us. On that basis, it will be hard to ever judge market penetration or dominance. So, maybe in terms of a vision from 15 years ago the growth of the semantic Web has been disappointing. But, for Fred and me, we are finally seeing the landscape clearly and in perspective, even if from a viewpoint that may be different from others’. From our vantage point, we are at the exciting cusp of a new, broader synthesis.

NOTE: This is Part I of a two-part series. Part II will appear shortly.

[1] Tim Berners-Lee, James Hendler, and Ora Lassila, “The Semantic Web,” in Scientific American 284(5): pp 34-43, 2001. See http://www.scientificamerican.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21&catID=2.
[2] For those with a spare 90 minutes or so, you may also want to view this panel session and debate that took place on “An OWL 2 Far?” at ISWC ’08 in Karlsruhe, Germany, on October 28, 2008. The panel was chaired by Peter F. Patel-Schneider (Bell Labs, Alcathor) with the panel members of Stefan Decker (DERI Galway), Michel Dumontier (Carleton University), Tim Finin (University of Maryland) and Ian Horrocks (University of Oxford), with much audience participation. See http://videolectures.net/iswc08_panel_schneider_owl/
[3] Open Biomedical Ontologies (OBO) is an effort to create controlled vocabularies for shared use across different biological and medical domains. As of 2006, OBO formed part of the resources of the U.S. National Center for Biomedical Ontology (NCBO). As of the date of this article, there were 376 ontologies listed on the NCBO’s BioOntology site. Both OBO and BioOntology provide tools and best practices.
[4] Fifth International Workshop on Consuming Linked Data (COLD 2014), co-located with the 13th International Semantic Web Conference (ISWC) in Riva del Garda, Italy, October 19-20.
[7] See the thread on the W3C semantic web mailing list beginning at http://lists.w3.org/Archives/Public/semantic-web/2014Jul/0129.html.
Posted:July 10, 2014

Open Semantic FrameworkMore Than 20 Are Currently Active and Often in Open Source

I have been periodically tracking ontology tools for some time now (also as contained on the Open Semantic Framework wiki). Recent work caused me to update the listing in the ontology matching/mapping/alignment area. Ontology alignment is important once one attempts to integrate across multiple knowledge bases. Steady progress in better performance (precision and recall) has been occurring, though efforts may have plateaued somewhat. Shvaiko and Euzenat have a good report on the state of the art in ontology alignment.

There has been a formalized activity on ontology alignment going back to 2003. This OAEI (Ontology Alignment Evaluation Initiative) has evolved to include formal tests and datasets, and annual evaluations and bake-offs. Over the years, various tools have come and gone, and some have evolved through multiple versions. Some are provided in source or with online demos; others are research efforts with no testable code.

As far as I know, no one has kept a current and comprehensive listing of these tools and their active status (though the Ontology Matching site does have an outdated list). Please accept the listing below as one attempt to redress this gap.

I welcome submissions of new (unlisted) tools, particularly those that are still active and available for download. There are surely gaps in what is listed below. Also, expect some new tools and updated results to be forthcoming from OAEI 2014 as reported at the Ontology Mapping workshop at ISWC effort in October.

Besides the tapering improvement in performance, other notable trends in ontology matching include ways to optimize multiple scoring methods and using background knowledge to help guide alignments.

Active, Often with Code

  • The Alignment API is an API and implementation for expressing and sharing ontology alignments. The correspondences between entities (e.g., classes, objects, properties) in ontologies is called an alignment. The API provides a format for expressing alignments in a uniform way. The goal of this format is to be able to share on the web the available alignments. The format is expressed in RDF, so it is freely extensible. The Alignment API itself is a Java description of tools for accessing the common format. It defines four main interfaces (Alignment, Cell, Relation and Evaluator)
  • AgreementMakerLight is an automated and efficient ontology matching system derived from AgreementMaker
  • Blooms is a tool for ontology matching. It utilizes information from Wikipedia category hierarchy and from the web to identify subclass relationship between entities. See also its Wiki page
  • CODI (Combinatorial Optimization for Data Integration) leverages terminological structure for ontology matching. The current implementation produces mappings between concepts, properties, and individuals. CODI is based on the syntax and semantics of Markov logic and transforms the alignment problem to a maximum-a-posteriori optimization problem
  • COMA++ is a schema and ontology matching tool with a comprehensive infrastructure. Its graphical interface supports a variety of interaction
  • Falcon-AO (Finding, aligning and learning ontologies) is an automatic ontology matching tool that includes the three elementary matchers of String, V-Doc and GMO. In addition, it integrates a partitioner PBM to cope with large-scale ontologies* hMAFRA (Harmonize Mapping Framework) is a set of tools supporting semantic mapping definition and data reconciliation between ontologies. The targeted formats are XSD, RDFS and KAON
  • GOMMA is a generic infrastructure for managing and analyzing life science ontologies and their evolution. The component-based infrastructure utilizes a generic repository to uniformly and efficiently manage many versions of ontologies and different kinds of mappings. Different functional components focus on matching life science ontologies, detecting and analyzing evolutionary changes and patterns in these ontologies
  • HerTUDA is a simple, fast ontology matching tool, based on syntactic string comparison and filtering of irrelevant mappings. Despite its simplicity, it outperforms many state-of-the-art ontology matching tools
  • Karma is an information integration tool to integrate data from databases, spreadsheets, delimited text files, XML, JSON, KML and Web APIs. Users integrate information according to an ontology of their choice using a graphical user interface that automates much of the process. Karma learns to recognize the mapping of data to ontology classes and then uses the ontology to propose a model that ties together these classes
  • KitAMO is a tool for evaluating ontology alignment strategies and their combinations. It supports the study, evaluation and comparison of alignment strategies and their combinations based on their performance and the quality of their alignments on test cases. Based on the SAMBO project
  • The linked open data enhancer (LODE) framework is a set of integrated tools that allow digital humanists, librarians, and information scientists to connect their data collections to the linked open data cloud. It can be applied to any domain with RDF datasets
  • LogMap is highly scalable ontology matching system with ‘built-in’ reasoning and diagnosis capabilities. LogMap can deal with semantically rich ontologies containing tens (and even hundreds) of thousands of classes
  • MapOnto is a research project aiming at discovering semantic mappings between different data models, e.g, database schemas, conceptual schemas, and ontologies. So far, it has developed tools for discovering semantic mappings between database schemas and ontologies as well as between different database schemas. The Protege plug-in is still available, but appears to be for older versions
  • MatchIT automates and facilitates schema matching and semantic mapping between different Web vocabularies. MatchIT runs as a stand-alone or plug-in Eclipse application and can be integrated with popular third party applications. MatchIT’s uses Adaptive Lexicon™ as an ontology-driven dictionary and thesaurus of English language terminology to quantify and ank the semantic similarity of concepts. It apparently is not available in open source
  • OntoM is one component of the OntoBuilder, which is a comprehensive ontology building and managing framework. OntoM provides a choice of mapping and scoring methods for matching schema
  • The Ontology Mapping Tool (OMT) is an Eclipse plug-in part of the Web Service Modeling Toolkit (WSMT), designed to offer support for the semi-automatic creation of ontology mappings. OMT offers a set of features such as multiple ontology perspectives, mapping contexts, suggestions, bottom-up and top-down mapping strategies
  • Optima is a state of the art general purpose tool for performing ontology alignment. It automatically identifies and matches relevant concepts between ontologies. The tool is supported by an intuitive user interface that facilitates the visualization and analysis of ontologies in N3, RDF and OWL and the alignment results. This is an open source ontology alignment frame work. Optima is also available as a plugin to Protégé ontology editor
  • PARIS is a system for the automatic alignment of RDF ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level
  • S-Match takes any two tree like structures (such as database schemas, classifications, lightweight ontologies) and returns a set of correspondences between those tree nodes which semantically correspond to one another
  • ServOMap is an ontology matching tool based on Information Retrieval technique relying on the ServO system. To run it, please follow the directions described at http://oaei.ontologymatching.org/2012/seals-eval.html
  • The Silk framework is a tool for discovering relationships between data items within different Linked Data sources. Data publishers can use Silk to set RDF links from their data sources to other data sources on the Web. While designed for mapping instance data, it can also be used for schema
  • Yam++ (not) Yet Another Matcher is a flexible and self-configuring ontology matching system for discovering semantic correspondences between entities (i.e., classes, object properties and data properties) of ontologies. This new version YAM++ 2013 has a significant improvement from the previous versions. See also the 2013 results. Code not apparently available.

Not Apparently in Active Use

  • ASMOV (Automated Semantic Mapping of Ontologies with Validation) is an automatic ontology matching tool which has been designed in order to facilitate the integration of heterogeneous systems, using their data source ontologies
  • The AMW (ATLAS Model Weaver) is a tool for establishing relationships (i.e., links) between models. The links are stored in a model, called weaving model
  • Chimaera is a software system that supports users in creating and maintaining distributed ontologies on the web. Two major functions it supports are merging multiple ontologies together and diagnosing individual or multiple ontologies
  • ConcepTool is a system to model, analyse, verify, validate, share, combine, and reuse domain knowledge bases and ontologies, reasoning about their implication
  • CMS (CROSI Mapping System) is a structure matching system that capitalizes on the rich semantics of the OWL constructs found in source ontologies and on its modular architecture that allows the system to consult external linguistic resources
  • ConRef is a service discovery system which uses ontology mapping techniques to support different user vocabularies
  • DRAGO reasons across multiple distributed ontologies interrelated by pairwise semantic mappings, with a vision of peer-to-peer mapping of many distributed ontologies on the Web. It is implemented as an extension to an open source Pellet OWL Reasoner
  • DSSim is an agent-based ontology matching framework; neither application nor source code appears to be available
  • FOAM is the Framework for ontology alignment and mapping. It is based on heuristics (similarity) of the individual entities (concepts, relations, and instances)
  • HMatch is a tool for dynamically matching distributed ontologies at different levels of depth. In particular, four different matching models are defined to span from surface to intensive matching, with the goal of providing a wide spectrum of metrics suited for dealing with many different matching scenarios that can be encountered in comparing concept descriptions of real ontologies
  • IF-Map is an Information Flow based ontology mapping method. It is based on the theoretical grounds of logic of distributed systems and provides an automated streamlined process for generating mappings between ontologies of the same domain
  • LILY is a system matching heterogeneous ontologies. LILY extracts a semantic subgraph for each entity, then it uses both linguistic and structural information in semantic subgraphs to generate initial alignments. The system is presently in a demo version only
  • MAFRA Toolkit – the Ontology MApping FRAmework Toolkit allows users to create semantic relations between two (source and target) ontologies, and apply such relations in translating source ontology instances into target ontology instances
  • Malasco is an ontology matching system for matching large-scale OWL ontologies. It can use different partitioning algorithms and existing matching tools
  • myOntology is used to produce the theoretical foundations, and deployable technology for the Wiki-based, collaborative and community-driven development and maintenance of ontologies instance data and mappings
  • OLA stands for OWL Lite Alignment. This is the name of a method for computing alignments between two OWL (non necessary Lite) ontologies
  • OntoEngine is a step toward allowing agents to communicate even though they use different formal languages (i.e., different ontologies). It translates data from a “source” ontology to a “target”
  • OntoMerge serves as a semi-automated nexus for agents and humans to find ways of coping with notational differences between ontologies with overlapping subject areas
  • The OWL-CTXMATCH application is a Java 5-compliant implementation of the OWL-CTXMATCH algorithm. Beside the Java platform it requires additional libraries and external data source that is WordNet 2.0
  • OLA/OLA2 (OWL-Lite Alignment) matches ontologies written in OWL. It relies on a similarity combining all the knowledge used in entity descriptions. It also deal with one-to-many relationships and circularity in entity descriptions through a fixpoint algorithm
  • OWLS-MX is a hybrid semantic Web service matchmaker. OWLS-MX 1.0 utilizes both description logic reasoning, and token based IR similarity measures. It applies different filters to retrieve OWL-S services that are most relevant to a given query
  • Potluck is a Web-based user interface that lets casual users—those without programming skills and data modeling expertise—mash up data themselves. Potluck is novel in its use of drag and drop for merging fields, its integration and extension of the faceted browsing paradigm for focusing on subsets of data to align, and its application of simultaneous editing for cleaning up data syntactically. Potluck also lets the user construct rich visualizations of data in-place as the user aligns and cleans up the data
  • PRIOR+ is a generic and automatic ontology mapping tool, based on propagation theory, information retrieval technique and artificial intelligence model. The approach utilizes both linguistic and structural information of ontologies, and measures the profile similarity and structure similarity of different elements of ontologies in a vector space model (VSM)
  • RiMOM (Risk Minimization based Ontology Mapping) integrates different alignment strategies: edit-distance based strategy, vector-similarity based strategy, path-similarity based strategy, background-knowledge based strategy, and three similarity-propagation based strategies
  • SAMBO is a system that assists a user in aligning and merging two ontologies in OWL format. The user performs an alignment process with the help of alignment suggestions proposed by the system. The system carries out the actual merging and derives the logical consequences of the merge operations
  • semMF is a flexible framework for calculating semantic similarity between objects that are represented as arbitrary RDF graphs. The framework allows taxonomic and non-taxonomic concept matching techniques to be applied to selected object properties
  • Snoggle is a graphical, SWRL-based ontology mapper. Snoggle attempts to solve the ontology mapping problem by providing a graphical user interface (similar to which of the Microsoft Visio) to guide the process of ontology vocabulary alignment. In Snoggle, user-defined mappings can be serialized into rules, which is expressed using SWRL
  • Terminator is a tool for creating term to ontology resource mappings (documentation in Finnish)
  • Vine is a tool that allows users to perform fast mappings of terms across ontologies. It performs smart searches, can search using regular expressions, requires a minimum number of clicks to perform mappings, can be plugged into arbitrary mapping framework, is non-intrusive with mappings stored in an external file, has export to text files, and adds metadata to any mapping. See also http://sourceforge.net/projects/vine/.

Posted by AI3's author, Mike Bergman Posted on July 10, 2014 at 11:57 am in Ontologies, Semantic Web Tools | Comments (1)
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Posted:June 30, 2014

Open Semantic Framework Structured Dynamics Moves to Integrate Key Initiatives

Structured Dynamics is pleased to announce its new UMBEL Web site and set of Web services.

Our first release of the UMBEL site occurred in 2007 while UMBEL was still under development. That site used its own homegrown HTML. The release was followed in 2008 by the addition of our own Web services. The Web services were well-received, which caused Structured Dynamics to develop the more general structWSF Web services framework (most recently updated as the OSF Web Services). We subsequently migrated the earlier UMBEL Web services to this more general framework, and also migrated to Drupal as the standard content management and Web site component for OSF.

For most reasons, including all client work to date, our OSF framework (Web services + Drupal 7) has been performant and met client site needs. However, the operation of the UMBEL Web services was often problematic after moving to the Drupal (full OSF) version. Unfortunately, we have seen both performance and stability problems, though calculations over a full 28,000 node graph are a challenge in any environment.

Since the UMBEL structure was an order of magnitude larger than our client work to date, we have frankly adopted a posture of occasional monitoring and reboots to keep the UMBEL Web site up. This posture was not limiting use of UMBEL for general browsing purposes, but was limiting its usefulness as a working API.

Because the cobbler’s son is often the last to get shoes, we have let the UMBEL Web site chill to a degree in the background. But, now, with other imperatives underway and some dedicated time to look directly at performance of larger-scale ontologies, we have looked at these items anew. The report card on our current evaluations is contained in a newly released UMBEL Web site with services, which I summarize and provide context for below. What emerges is an interesting story of discovery and growth.

Basis of the New Site

The new UMBEL site and its underlying 28,000 concept graph is consistent with the OSF layered architecture. However, the Web services are now written in Clojure and the Web site framework uses Bootstrap and plain ol’ HTML. These structured and foundational changes have been championed by Fred Giasson, SD’s chief technology officer, who is also putting forth a blog series on Clojure in particular. He also has a current post from a technical basis on these UMBEL site and service changes.

In essence, we have learned two important things about our prior practice with respect to making UMBEL Web services broadly available. First, for UMBEL, we do not need or want our standard configuration of having a Drupal front-end as the interface into OSF. Access to a knowledge graph does not need — and is ill-served — by having a complicated interface stand atop a large-scale concept model. APIs and Web services are the most important interaction points with the UMBEL knowledge graph, not a user-oriented Web site.

Second, in the various phases of our work, we had come to embrace the idea of ontology-driven applications (what we have termed ODapps). The compelling vision behind such structures is to place the emphasis on knowledge structures and data, rather than more software. Once one begins to unpack that vision, it can also become clear that software programming languages themselves that look toward “code as data” might be one way to be consistent with that vision.

Seeking a Sense of Harmony

For years I have been writing about data integration and interoperability and our company has been devoted to the topic. I have written extensively about the importance of RDF and description logics to how we organize and represent data. We were also some of the first to supplement RDF with a faceted text-search engine (Solr) to provide the most responsive query environment across structured to unstructured data. We have also adopted ontologies and the OWL 2 (plus SKOS) languages as standards to both foster and enable interoperability. We have explored native data structs to understand how wild forms of information can be efficiently pipelined into interoperable RDF and text forms.

All of this points to the ideal of the democratization of the information function in the enterprise. In other words, to the idea that how data structures get organized and represented (the ontology side of things) is something that knowledge workers can do themselves, rather than accepting the bottleneck of IT and programmers.

This is well and good except there is a critical “last mile” between data representation and data usefulness. This “last mile” deals with how actual data gets manipulated and then organized and presented (visualized). Query responses, reports, analysis and maps continue to be the choke points between knowledge workers and their IT support. And one need not frame this entirely from an enterprise perspective: these same challenges exist for the individual researcher or the small organization.

So, while one can focus on data and its organization and representation, until we address this “last mile” problem, we still are not likely addressing the largest source of frustration and lost opportunities in the knowledge function.

The reason that simple data struct forms and tools like spreadsheets continue to be popular is that they are empirically the best tools for the “last mile”. Web forms and services are increasingly showing their strengths in this realm.

Once one steps back and looks at the entire cycle from basic datum to actionable knowledge, it is clear that the question of data model is but one portion of the challenge. The remaining challenge is how (now) accessible information can be placed into context and acted upon. Further, if one premise is the democratization of the information function, then the challenge should also be how to provide productive capabilities for the last mile to the knowledge worker. Productivity is enhanced when there are the fewest channels and distortions between signal (problem) and channel (user chains).

Fred, in his investigation of functional languages, clearly saw that bringing the languages of code (programming) into the language of data (knowledge workers as expressed in our RDF world view) was one means to reduce the number and lossiness of the channels between problem (signal) and solution. A world view premised on the efficient representation and interoperability of data must logically support the idea of a coding (instructional or language) base aligned as well to problems. Moreover, since software guides the actual computer operations, a form of the software that supports the nature of the data should also provide a more performant framework for moving forward. In technical terms, this is known as homoiconicity.

Whether one looks to the intellectual foundations of Charles S Peirce or Claude Shannon (both of whom we do), one can see that the idea of signs and information theory means finding both data representations and code that minimize communication losses and promote the accurate transfer of the message. Lossless data transmission is one contributor to that vision, but so too is a functional representation for how the information is to be processed and transformed that aligns most closely with the information at hand.

Ergo, a better model for data is not enough. A better model of how to manipulate that data (that is, software) is also needed that aligns with the idea of coherence and structure in the underlying information. For our purposes, we have chosen Clojure as the functional language basis for these new UMBEL Web services. Not only is it performant, but it aligns well with the creation of domain-specific languages (DSLs) that also promise to democratize the computing function for the knowledge worker.

Bringing the Pieces Together

Fred and I founded Structured Dynamics a bit more than five years ago. But, we had worked together much earlier on UMBEL and Zitgist. For nearly ten years now, we have episodically emphasized a few different initiatives and passions.

One of those passions has been the structure of data and information. It is this perspective that brought us to RDF and data structs (and our irON efforts) at various times. The idea of structure is a basis for our company name, and represents the belief that structure can be brought to unstructured forms (via tagging, for example). Structure is perhaps the most common notion or concept in my own writings for a decade.

Another need has been the idea of making semantic technologies operational. We have been keen researchers of the tools space and algorithms and such since the beginning. We observed early on that many innovative and open source semantic programs existed, but most were the result of EU grants or academic efforts elsewhere. Thousands of tools existed, but very few had either been evaluated or stress-tested. By bringing together the best of class tools and integrating them, we could begin to provide a useful semantic platform for enterprises. This motivation was the genesis for the Open Semantic Framework, and has been the major source of our client support since SD was founded. We have finally created an enterprise-capable platform and have done much to transfer its technology. But, these concepts are difficult, and much remains to be done before semantic technologies are a standard option for enterprises.

Still, in another vein, our first love and interest has been knowledge bases. We first identified the need for UMBEL years ago when we perceived an organizing vocabulary would become an essential glue on the Web. We pursued and studied Wikipedia and how it is informing knowledge bases. Instance data and how it is represented is a passion for how these knowledge bases (KBs) get leveraged going forward.

As a smaller consulting and development boutique, we have needed to be opportunistic about when and where we devoted efforts to these pieces. So, over the months or years, we have at various times devoted ourselves to data models and ontologies (structure), the Open Semantic Framework (platform), or UMBEL or Wikipedia (KBs, knowledge bases). Depending on funding and priorities, any one of these threads did receive episodic attention and focus. But, truth is, each one of these pieces has been developed in (project-level) isolation to the whole. Such piecemeal development was essential until each component achieved an appropriate degree of maturity.

I could say we could foresee some years back that all of these pieces would eventually reinforce and bolster one another. Though there is a small bit of truth in that statement, the way things have actually unfolded is to show, as experience and sophistication have been gained, that there is a synergy that comes in the interplay of these various pieces. The goodness is that Structured Dynamics’ efforts (and of its predecessors) were building inexorably to the possible cross-fertilization of these efforts.

Once this kind of realization takes place — that data, code and semantics move hand-in-hand — it then becomes logical to look at the entire knowledge ecosystem. For example, it is not surprising that artificial intelligence, now in the informed guise of KB-backed systems, has again come to the fore. It is also not surprising that what software and programming languages we bring to bear also directly interact with these concerns. Just as Hadoop and non-relational database systems have become prominent, we should also investigate what kind of programming languages and constructs may best fit into this brave new information world.

What we have seen from that investigation is that functional languages (with their DSL offspring) somehow fit into the overall equation moving forward. SD has moved from a single-focus endeavor to one explicitly looking at integration and interoperability issues. What we had earlier seen as (largely) independent pieces we now see as fitting into a broader equation of related emphases:

Structure + Platform + KBs + Functional Language = Knowledge Worker-based Interoperability

We are seeing artificial intelligence moving in these directions. As a subset of AI, I suspect we will also see  the semantic Web moving in the same direction.

We clearly now have the theory, the data, the understanding of semantics, and languages and data representations that can make these democratic interoperabilities become real. This new UMBEL Web site is the first expression of how these pieces can begin to work together into a compelling, accessible whole.

We welcome you to visit and to take advantage of UMBEL’s fully accessible APIs.

Posted:August 12, 2012

Example Ontology GrowthThe Transition from Transactions to Connections

Virtually everywhere one looks we are in the midst of a transition for how we organize and manage information, indeed even relationships. Social networks and online communities are changing how we live and interact. NoSQL and graph databases — married to their near cousin Big Data — are changing how we organize and store information and data. Semantic technologies, backed by their ontologies and RDF data model, are showing the way for how we can connect and interoperate disparate information in ways only dreamed about a decade ago. And all of this, of course, is being built upon the infrastructure of the Internet and the Web, a global, distributed network of devices and information that is undoubtedly one of the most important technological developments in human history.

There is a shared structure across all of these developments — the graph. Graphs are proving to be the new universal paradigm for how we organize and manage information. Graphs have an inherently expandable nature, and one which can also capture any existing structure. So, as we see all of the networks, connections, relationships and links — both physical and informational — grow around us, it is useful to step back a bit and contemplate the universal graph structure at the core of these developments.

Understanding that we now live in the Age of the Graph means we can begin studying and using the concept of the graph itself to better analyze and manage our interconnected world. Whether we are trying to understand the physical networks of supply chains and infrastructure or the information relationships within ontologies or knowledge graphs, the various concepts underlying graphs and graph theory, themselves expressed through a rich vocabulary of terms, provide the keys for unlocking still further treasures hidden in the structure of graphs.

Graphs as a Concept

The use of “graph” as a mathematical concept is not much more than 100 years old. The beginning explication of the various classes of problems that can be addressed by graph theory probably is no older than 300 years. The use of graphs for expressing logic structures probably is not much older than 100 years, with the intellectual roots beginning with Charles Sanders Peirce [1]. Though likely trade routes and their affiliated roads and primitive transportation or nomadic infrastructures were perhaps the first expressions of physical networks, the emergence and prevalence of networks is a fairly recent phenomenon. The Internet and the Web are surely the catalyzing development that has brought graphs and networks to the forefront.

In mathematics, a graph is an abstract representation of a set of objects where pairs of the objects are connected. The objects are most often known as nodes or vertices; the connections between the objects are called edges. Typically, a graph is depicted in diagrammatic form as a set of dots or bubbles for the nodes, joined by lines or curves for the edges. If there is a logical relationship between connected nodes the edge is directed, and the graph is known as a directed graph. Various structures or topologies can be expressed through this conceptual graph framework. Graphs are one of the principle focuses of study in discrete mathematics [2]. The word “graph” was first used in the sense as a mathematical structure by J.J. Sylvester in 1878 [3].

As representative of various data models, particularly in our company’s own interests in the Resource Description Framework (RDF) model, the nodes can represent “nouns” or subjects or objects (depending on the direction of the links) or attributes. The edges or connections represent “verbs” or relationships, properties or predicates. Thus, the simple “triple” of the basic statement in RDF (consisting of subjectpredicateobject) is one of the constituent barbells that make up what becomes the eventual graph structure.

The manipulation and analysis of graph structures comes under the rubric of graph theory. The first recognized paper in that field is the Seven Bridges of Königsberg, written by Leonhard Euler in 1736. The objective of the paper was to find a walking path through the city that would cross each bridge once and only once. Euler proved that the problem has no solution:

Seven Bridges of Königsberg; from Wikipedia –> Seven Bridges of Königsberg graph; from Wikipedia

Euler’s approach represented the path problem as a graph, by treating the land masses as nodes and the bridges as edges. Euler’s proof postulated that if every bridge has been traversed exactly once, it follows that, for each land mass (except for the ones chosen for the start and finish), the number of bridges touching that land mass must be even (the number of connections to a node we now call “degree”). Since that is not true for this instance, there is no solution. Other researchers, including Leibniz, Cauchy and L’Huillier applied this approach to similar problems, leading to the origin of the field of topology.

Later, Cayley broadened the approach to study tree structures, which have many implications in theoretical chemistry. By the 20th century, the fusion of ideas coming from mathematics with those coming from chemistry formed the origin of much of the standard terminology of graph theory.

The Theory of Graphs

Graph theory forms the core of network science, the applied study of graph structures and networks. Besides graph theory, the field draws on methods including statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. Classical problems embraced by this realm include the four color problem of maps, the traveling salesman problem, and the six degrees of Kevin Bacon.

Graph theory and network science are the suitable disciplines for a variety of information structures and many additional classes of problems. This table lists many of these applicable areas, most with links to still further information from Wikipedia:

Graph Structures Graph Problems
Data structures
Tree structures
List structures
Matrix structures
Path structures
Networks
Logic structures
Random graphs
Weighted graphs
Sparse/dense graphs
Enumeration
Subgraphs, induced subgraphs, and minors
Search and navigation
Graph coloring
Subsumption and unification
Route (path) problems
Matrix manipulations (many)
Network flow
Visibility graph problems
Covering problems
Graph structure
Graph classes

Graphs are among the most ubiquitous models of both natural and human-made structures. They can be used to model many types of relations and process dynamics in physical, biological and social systems. Many problems of practical interest can be represented by graphs. This breadth of applicability makes network science and graph theory two of the most critical analytical areas for study and breakthroughs for the foreseeable future. I touch on this more in the concluding section.

Graphs as Physical Networks

Surely the first examples of graph structures were early trade and nomadic routes. Here, for example, are the trade routes of the Radhanites dating from about 870 AD [4]:

Trade network of the Radhanites, c. 870 CE; from Wikipedia

It is not surprising that routes such as these, or other physical networks as exemplified by the bridges of Königsberg, were the stimulus for early mathematics and analysis related to efficient use of networks. Minimizing the time to complete a trade circuit or visiting multiple markets efficiently has clear benefits. These economic rationales apply to a wide variety of modern, physical networks, including:

Of course, included in the latter category is the Internet itself. It is the largest graph in existence, with an estimated 2.2 billion users and their devices all connected in one way or another in all parts of the globe [5].

Graphs as Natural Systems

Graphs and graph theory also have broad applicability to natural systems. For example, graph theory is used extensively to study molecular structures in chemistry and physics. A graph makes a natural model for a molecule, where vertices represent atoms and edges bonds. Similarly, in biology or ecology, graphs can readily express such systems as species networks, ecological relationships, migration paths, or the spread of diseases. Graphs are also proper structures for modeling biological and chemical pathways.

Some of the exemplar natural systems that lend themselves to graph structures include:

As with physical networks, a graph representation for natural systems provides real benefits in computer processing and analysis. Once expressed as a graph, all graph algorithms and perspectives from graph theory and network science can be brought to bear. Statistical methods are particularly applicable to representing connections between interacting parts of a system, as well to representing the physical dynamics of natural systems.

Graphs as Social Networks

Parallel with the growth of the Internet and Web has been the growth of social networks. Social network analysis (SNA) has arguably been the single most important driver for advances in graph theory and analysis algorithms in recent years. New and interesting problems and challenges — from influence to communities to conflicts — are now being elucidated through techniques pioneered for SNA.

Second only in size to the Internet has been the graph of interactions arising from Facebook. Facebook had about 900 million users as of May 2012, half of which accessed the service via mobile devices [6]. Facebook famously embraced the graph with its own Open Graph protocol, which makes it easy for users to access and tie into Facebook’s social network. A representation of the Facebook social graph as of December 2010 is shown in this well-known figure:

The suitability of the graph structure to capture relationships has been a real boon to better understanding of social and community dynamics. Many new concepts have been introduced as the result of SNA, including such things as influence, diversity, centrality, cliques and so forth. (The opening diagram to this article, for example, models centrality, with blue the maximum and red the minimum.)

Particular areas of social interaction that lend themselves to SNA include:

Entirely new insights have arisen from SNA including finding terrorist leaders, analyzing prestige, or identifying keystone vendors or suppliers in business ecosystems.

Graphs as Information Representations

Given the ubiquity of graphs as representations of real systems and networks, it is certainly not surprising to see their use in computer science as as means for information representation. We already saw in the table above the many data structures that can be represented as graphs, but the paradigm has even broader applicability.

The critical breakthroughs have come through using the graph as a basis for data models and logic models. These, in turn, provide the basis for crafting entire graph-based vocabularies and languages. Once such structures are embraced, it is a natural extension to also extend the mindset to graph databases as well.

Some of the notable information representations that have a graph as their basis include:

Graphs as Knowledge Representations

A key point of graphs noted earlier was their inherent extensibility. Once graphs are understood as a great basis for representing both logic and data structures, it is a logical next step to see their applicability extend to knowledge representations and knowledge bases as well.

Graph-theoretic methods have proven particularly useful in linguistics, since natural language often lends itself well to discrete structure. So, not only can graphs represent syntactic and compositional structure, but they can also capture the interrelationships of terms and concepts within those languages. The usefulness of graph theory to linguistics is shown by the various knowledge bases such as WordNet (in various languages) and VerbNet.

Domain ontologies are similar structures, capturing the relationships amongst concepts within a given knowledge domain. These are also known as knowledge graphs, and Google has famously just released its graph of entities to the world [7]. Semantic networks and neural networks are similar knowledge representations.

The following interactive diagram, of the UMBEL knowledge graph of about 25,000 reference concepts for helping to orient disparate datasets [8], shows that some of these graph structures can get quite large:

Notes: at standard resolution, if this graph were to be rendered in actual size, it would be larger than 34 feet by 34 feet square at full zoom !!! Hint: that is about 1200 square feet, or 1/2 the size of a typical American house ! Also, if you are viewing this in a feed reader, click here to see the interactive graph.

What all of these examples show is the nearly universal applicability of graphs, from the abstract to the physical, from the small to the large, and every gradation between. We also see how basic graph structures and concepts can be built upon with more structure. This breadth points to the many synergies and innovations that may be transferred from diverse fields to advance the usefulness of graph theories.

Graphs as a Guiding Paradigm

Despite the many advances that have occurred in graph theory and the increased attention from social network analysis, many, many graph problems remain some of the hardest in computation. Optimizations, partitioning, mapping, inferencing, traversing and graph structure comparisons remain challenging. And, some of these challenges are only growing due to the growth in the size of networks and graphs.

Applying the lessons of the Internet in such areas as non-relational databases, distributed processing, and big data and map reduce-oriented approaches will help some in this regard. We’re learning how to divide and conquer big problems, and we are discovering data and processing architectures more amenable to graph-based problems.

The fact we have now entered the Age of the Graph also bodes that further scrutiny and attention will lead to more analytic breakthroughs and innovation. We may be in an era of Big Data, but the structure underlying all of that is the graph. And that reality, I predict, will result in accelerated advances in graph theory.


[1] For a fairly broad discussion of Peirce in relation to these topics, see M.K. Bergman, 2012. “Give Me a Sign: What Do Things Mean on the Semantic Web?,” in AI3:::Adaptive Innovation blog, January 24, 2012. See http://www.mkbergman.com/994/give-me-a-sign-what-do-things-mean-on-the-semantic-web/.
[2] Topics in discrete mathematics, which are all applicable to graphing techniques and theory, include theoretical computer science, information theory, logic, set theory, combinatorics, probability, number theory, algebra, geometry, topology, discrete calculus or discrete analysis, operations research, game theory, decision theory, utility theory, social choice theory, and all discrete analogues of continuous mathematics.
[3] See reference 1 in the Wikipedia entry on graph theory.
[4] According to Wikipedia, the Radhanites were medieval Jewish merchants involved in trade between the Christian and Islamic worlds during the early Middle Ages (approx. 500–1000 AD). Many trade routes previously established under the Roman Empire continued to function during that period largely through their efforts. Their trade network covered much of Europe, North Africa, the Middle East, Central Asia and parts of India and China.
[5] See the article on the Internet in Wikipedia for various size estimates.
[6] See the article on the Facebook in Wikipedia for various size estimates.
[7] For my discussion of the Google Knowledge Graph, see M.K. Bergman, 2012. “Deconstructing the Google Knowledge Graph,” in AI3:::Adaptive Innovation blog, May 18, 2012. See http://www.mkbergman.com/1009/deconstructing-the-google-knowledge-graph/.
[8] UMBEL (the Upper Mapping and Binding Exchange Layer) is designed to help content interoperate on the Web. It provides two functions: a) it is a broad, general reference structure of 25,000 concepts, which provides a scaffolding to link and interoperate other datasets and domain vocabularies, and b) it is a base vocabulary for the construction of other concept-based domain ontologies, also designed for interoperation.