Posted:November 26, 2010

There’s an Endless Variety of World Views, and Almost as Many Ways to Organize and Describe ThemFriday     Brown Bag Lunch

Ontology is one of the more daunting terms for those exposed for the first time to the semantic Web. Not only is the word long and without many common antecedents, but it is also a term that has widely divergent use and understanding within the community. It can be argued that this not-so-little word is one of the barriers to mainstream understanding of the semantic Web.

The root of the term is the Greek ontos, or being or the nature of things. Literally — and in classical philosophy — ontology was used in relation to the study of the nature of being or the world, the nature of existence. Tom Gruber, among others, made the term popular in relation to computer science and artificial intelligence about 15 years ago when he defined ontology as a “formal specification of a conceptualization.”

While there have been attempts to strap on more or less formal understandings or machinery around ontology, it still has very much the sense of a world view, a means of viewing and organizing and conceptualizing and defining a domain of interest. As is made clear below, I personally prefer a loose and embracing understanding of the term (consistent with Deborah McGuinness’s 2003 paper, Ontologies Come of Age [1]).

There has been a resurgence of interest in ontologies of late. Two reasons have been the emergence of Web 2.0 and tagging and folksonomies, as well as the nascent emergence of the structured Web. In fact, on April 23-24 one of the noted communities of practice around ontologies, Ontolog, sponsored the Ontology Summit 2007 ,”Ontology, Taxonomy, Folksonomy: Understanding the Distinctions.”

These events have sparked my preparing this guide to ontologies. I have to admit this is a somewhat intrepid endeavor given the wealth of material and diversity of opinions.

Friday      Brown Bag Lunch This Friday brown bag leftover was first placed into the AI3 refrigerator more than three years ago on May 16, 2007. This reprise is unchanged since its original posting, though there is a more recent executive-level intro to ontologies on the OpenStructsTechWiki.

Overview and Role of Ontologies

Of course, a fancy name is not sufficient alone to warrant an interest in ontologies. There are reasons why understanding, using and manipulating ontologies can bring practical benefit:

  • Depending on their degree of formalism (an important dimension), ontologies help make explicit the scope, definition, and language and meaning (semantics) of a given domain or world view
  • Ontologies may provide the power to generalize about their domains
  • Ontologies, if hierarchically structured in part (and not all are), can provide the power of inheritance
  • Ontologies provide guidance for how to correctly “place” information in relation to other information in that domain
  • Ontologies may provide the basis to reason or infer over its domain (again as a function of its formalism)
  • Ontologies can provide a more effective basis for information extraction or content clustering
  • Ontologies, again depending on their formalism, may be a source of structure and controlled vocabularies helpful for disambiguating context; they can inform and provide structure to the “lexicons” in particular domains
  • Ontologies can provide guiding structure for browsing or discovery within a domain, and
  • Ontologies can help relate and “place” other ontologies or world views in relation to one another; in other words, ontologies can organize ontologies from the most specific to the most abstract.

Both structure and formalism are dimensions for classifying ontologies, which combined are often referred to as an ontology’s “expressiveness.” How one describes this structure and formality differs. One recent attempt is this figure from the Ontology Summit 2007‘s wrap-up communique:

Ontology Summit 2007 Communique Diagram

Note the bridging role that an ontology plays between a domain and its content. (By its nature, every ontology attempts to “define” and bound a domain.) Also note that the Summit’s 50 or so participants were focused on the trade-off between semantics v. pragmatic considerations. This was a result of the ongoing attempts within the community to understand, embrace and (possibly) legitimize “less formal” Web 2.0 efforts such as tagging and the folksonomies that can result from them.

There is an M.C. Escher-like recursion of the lizard eating its tail when one observes ontologists creating an ontology to describe the ontological domain. The above diagram, which itself would be different with a slight change in Summit participation or editorship, is, of course, but one representative view of the world. Indeed, a tremendous variety of scientific and research disciplines concern themselves with classifying and organizing the “nature of things.” Those disciplines go by such names as logicians, taxonomists, philosophers, information architects, computer scientists, librarians, operations researchers, systematicists, statisticians, historians, and so forth. (In short, given our ontos, every area of human endeavor has the urge to classify, to organize.) In each of these areas not only do their domains differ, but so do the adopted structures and classification schemes often used.

There are at least 40 terms or concepts across these various disciplines, most related to Web and general knowledge content, that have organizational or classificatory aspects that — loosely defined — could be called an “ontology” framework or approach:

Actual domains or subject coverage are then mostly orthogonal to these approaches.

Loosely defined, the number of possible ontologies is therefore close to infinite: domain X perspective X schema. (Just kidding — sort of! In fact, UMBC’s Swoogle ontology search service claims 10,000 ontologies presently on the Web; the actual data from August 2006 ranges from about 16,000 to 92,000 ontologies, depending on how “formal” the definition. These counts are also limited to OWL-based ontologies.)

Many have misunderstood the semantic Web because of this diversity and the slipperiness of the concept of an ontology. This misunderstanding becomes flat wrong when people claim the semantic Web implies one single grand ontology or organizational schema, One Ring to Rule Them All. Human and domain diversities makes this viewpoint patently false.

Diversity, ‘Naturalness’ and Change

The choice of an ontological approach to organize Web and structured content can be contentious. Publishers and authors perhaps have too many choices: from straight Atom or RSS feeds and feeds with tags to informal folksonomies and then Outline Processor Markup Language or microformats. From there, the formalism increases further to include the standard RDF ontologies such as SIOC (Semantically-Interlinked Online Communities), SKOS (Simple Knowledge Organizing System), DOAP (Description of a Project), and FOAF (Friend of a Friend) and the still greater formalism of OWL’s various dialects.

Arguing which of these is the theoretical best method is doomed to failure, except possibly in a bounded enterprise environment. We live in the real world, where multiple options will always have their advocates and their applications. All of us should welcome whatever structure we can add to our information base, no matter where it comes from or how it’s done. The sooner we can embrace content in any of these formats and convert it to a canonical form, we can then move on to needed developments in semantic mediation, the threshold condition for the semantic Web.

There are at least 40 concepts — loosely defined — that could be called an “ontology” framework or approach.

So, diversity is inevitable and should be accepted. But that observation need not also embrace chaos.

In my early training in biological systematics, Ernst Haeckel’s recapitulation theory that “ontogeny recapitulates phylogeny” (note the same ontos root, the difference from ontology being growth v. study) was losing favor fast. The theory was that the development of an organism through its embryological phases mirrors its evolutionary history. Today, modern biologists recognize numerous connections between ontogeny and phylogeny, explain them using evolutionary theory, or view them as supporting evidence for that theory.

Yet, like the construction of phylogenetic trees, systematicists strive for their classifications of the relatedness of organisms to be “natural”, to reflect the true nature of the relationship. Thus, over time, that understanding of a “natural” system has progressed from appearance → embryology → embryology + detailed morphology → species and interbreeding → DNA. While details continue to be worked out, the degree of genetic relatedness is now widely accepted by biologists as a “natural” basis for organizing the Tree of Life.

It is not unrealistic to also seek “naturalness” in the organization of other knowledge domains, to seek “naturalness” in the organization of their underlying ontologies. Like natural systems in biology, this naturalness should emerge from the shared understandings and perceptions of the domain’s participants. While subject matter expertise and general and domain knowledge are essential to this development, they are not the only factors. As tagging systems on the Web are showing, common usage and broad acceptance by the community at hand is important as well.

While it may appear that a domain such as the biological relatedness of organisms is more empirical than the concepts and ambiguous words in most domains of human endeavor, these attempts at naturalness are still not foolish. The phylogeny example shows that understanding changes over time as knowledge is gained. We now accept DNA over the recapitulation theory.

As the formal SKOS organizational schema for knowledge systems recognizes (see below), the ideas of narrower and broader concepts can be readily embraced, as well as concepts of relatedness and aliases (synonyms). These simple constructs, I would argue, plus the application of knowledge being gained in related domains, will enable tomorrow’s understandings to be more “natural” than today’s, no matter the particular domain at hand.

So, in seeking a “naturalness” within our organizational schema, we can also see that change is a constant. We also see that the tools and ideas underlying the seemingly abstract cause of merging and relating existing ontologies to one another will further a greater “naturalness” within our organizations of the world.

A Spectrum of Formalisms

According to the Summit, expressiveness is the extent and ease by which an ontology can describe domain semantics. Structure they define as the degree of organization or hierarchical extent of the ontology. They further define granularity as the level of detail in the ontology. And, as the diagram above alludes, they define other dimensions of use, logical basis, purpose and so forth of an ontology.

The over fifty respondents from 42 communities submitted some 70 different ontologies under about 40 terms to a survey that was used by the Summit to construct their diagram. These submissions included:

. . . formal ontologies (e.g., BFO, DOLCE, SUMO), biomedical ontologies (e.g., Gene Ontology, SNOMED CT, UMLS, ICD), thesauri (e.g., MeSH, National Agricultural Library Thesaurus), folksonomies (e.g., Social bookmarking tags), general ontologies (WordNet, OpenCyc) and specific ontologies (e.g., Process Specification Language). The list also includes markup languages (e.g., NeuroML), representation formalisms (e.g., Entity-Relation model, OWL, WSDL-S) and various ISO standards (e.g., ISO 11179). This [Ontolog] sample clearly illustrates the diversity of artifacts collected under “ontology”.

I think the simplest spectrum for such distinctions is the formalism of the ontology and its approach (and language or syntax, not further discussed here). More formal ontologies have greater expressiveness and structure and inferential power, less formal ones the opposite. Constructing more formal ontologies is more demanding, and takes more effort and rigor, resulting in an approach that is more powerful but also more rigid and less flexible. Like anything else, there are always trade-offs.

Based on work by Leo Obrst of Mitre as interpreted by Dan McCreary, we can view this as a trade-off as one of semantic clarity v. the time and money required to construct the formalism [12, 13]:

Structure and Formalism Increases Semantic Expressiveness
[Click on image for full-size pop-up]

Note this diagram reflects the more conventional, practitioner’s view of the “formal” ontology, which does not include taxonomies or controlled vocabularies (for example) in the definition. This represents the more “closely defined” end of the ontology (semantic) spectrum.

However, since we are speaking here of ontologies and the structured Web or the semantic Web, I believe we need to embrace a concept of ontology aligned to actual practice. Not all content providers can or want to employ ontology engineers to enable formal inferencing of their content. Yet, on the other hand, their content in its various forms does have some meaningful structure, some organization. The trick is to extract this structure for more meaningful use such as data exchange or data merging.

Ontology Approaches on the Web

Under such “loosely defined” bases we can thus see a spectrum of ontology approaches on the Web, proceeding from less structure and formalism to more so:

Type or Schema Examples Comments on Structure and Formalism
Standard Web Page entire Web General metadata fields in the and internal HTML codes and tags provide minimal, but useful sources of structure; other HTTP and retrieval data can also contribute
Blog / Wiki Page examples from Technorati, Bloglines, Wikipedia Provides still greater formalism for the organization and characterization of content (subjects, categories, posts, comments, date/time stamps, etc.). Importantly, with the addition of plug-ins, some of the basic software may also provide other structured characterizations or output (SIOC, FOAF, etc.; highly varied and site-specific given the diversity of publishing systems and plug-ins)
RSS / Atom feeds most blogs and most news feeds RSS extends basic XML schema for more robust syndication of content with a tightly controlled vocabulary for feed concepts and their relationships. Because of its ubiquity, this is becoming a useful baseline of structure and formalism; also, the nature of adoption shows much about how ontological structure is an artifact, not driver, for use
RSS / Atom feeds with tags or OPML Grazr, most newsfeed aggregators can import and export OPML lists of RSS feeds The OPML specification defines an outline as a hierarchical, ordered list of arbitrary elements. The specification is fairly open which makes it suitable for many types of list data. See also OML and XOXO
Hierarchical Faceted Metadata XFML, Flamenco These and related efforts from the information architecture (IA) community are geared more to library science. However, they directly contribute to faceted browsing, which is one of the first practical instantiations of the semantic Web
Folksonomies Flickr, del.icio.us Based on user-generated tags and informal organizations of the same; not linked to any “standard” Web protocols. Both tags and hierarchical structure are arbitrary, but some researchers now believe over large enough participant sets that structural consensus and value does emerge
Microformats Example formats include hAtom, hCalendar, hCard, hReview, hResume, rel-directory, xFolk, XFN and XOXO A microformat is HTML mark up to express semantics with strictly controlled vocabularies. This markup is embedded using specific HTML attributes such as class, rel, and rev. This method is easy to implement and understand, but is not free-form
Embedded RDF RDFa, eRDF An embedded format, like microformats, but free-form, and not subject to the approval strictures associated with microformats
Topic Maps Infoloom, Topic Maps Search Engine A topic map can represent information using topics (representing any concept, from people, countries, and organizations to software modules, individual files, and events), associations (which represent the relationships between them), and occurrences (which represent relationships between topics and information resources relevant to them)
RDF Many; DBpedia, etc. RDF has become the canonical data model since it represents a “universal” conversion format
RDF Schema SKOS, SIOC, DOAP, FOAF RDFS or RDF Schema is an extensible knowledge representation language, providing basic elements for the description of ontologies, otherwise called RDF vocabularies, intended to structure RDF resources. This becomes the canonical ontology common meeting ground
OWL Lite Here are some existing OWL ontologies; also see Swoogle for OWL search facilities The Web Ontology Language (OWL) is a language for defining and instantiating Web ontologies. An OWL ontology may include descriptions of classes, along with their related properties and instances. OWL is designed for use by applications that need to process the content of information instead of just presenting information to humans. It facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDF-S) by providing additional vocabulary along with a formal semantics. The three language versions are in order of increasing expressiveness
OWL DL
OWL Full
Higher-order “formal” and “upper-level” ontologies SUMO, DOLCE, PROTON, BFO, Cyc, OpenCyc These provide comprehensive ontologies and often related knowledge bases, with the goal of enabling AI applications to perform human-like reasoning. Their reasoning languages often use higher-order logics

As a rule of thumb, items that are less “formal” can be converted to a more formal expression, but the most formal forms can generally not be expressed in less formal forms.

As latter sections elaborate, I see RDF as the universal data model for representing this structure into a common, canonical format, with RDF Schema (specifically SKOS, but also supplemented by FOAF, DOAP and SIOC) as the organizing ontology knowledge representation language (KRL).

This is not to say that the various dialects of OWL should be neglected. In bounded environments, they can provide superior reasoning power and are warranted if they can be sufficiently mandated or enforced. But the RDF and RDF-S systems represent the most tractable “meeting place” or “middle ground,” IMHO.

Still-Another “Level” of Ontologies

As if the formalism dimension were not complicated enough, there is also the practice within the ontology community to characterize ontologies by “levels”, specifically upper, middle and lower levels. For example, chances are that you have heard particularly of “upper-level” ontologies.

The following figure helps illustrate this “level” dimension. This diagram is also from Leo Obrst of Mitre [12], and was also used in another 2006 talk by Jack Park and Patrick Durusau (discussed further below for other reasons):

Ontology Levels

Examples of upper-level ontologies include the Suggested Upper Merged Ontology (SUMO), the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), PROTON, Cyc and BFO (Basic Formal Ontology). Most of the content in their upper-levels is akin to broad, abstract relations or concepts (similar to the primary classes, for example, in a Roget’s Thesaurus — that is, real ontos stuff) than to “generic common knowledge.” Most all of them have both a hierarchical and networked structure, though their actual subject structure relating to concrete things is generally pretty weak [2].

The above diagram conveys a sense of how multiple ontologies can relate to one another both in terms of narrower and broader topic matter and at the same “levels” of generalization. Such “meta-structure” (if you will) can provide a reference structure for relating multiple ontologies to one another.

The relationships and mappings amongst ontologies is a critical infrastructure component of the semantic Web.

It resides exactly in such bindings or relationships that we can foresee the promise of querying and relating multiple endpoints on the Web with accurate semantics in order to connect dots and combine knowledge bases. Thus, the understanding of the relationships and mappings amongst ontologies becomes a critical infrastructural component of the semantic Web.

The SUMO Example

We can better understand these mapping and inter-relationship concepts by using a concrete example with a formal ontology. We’ll choose to use the Suggested Upper Merged Ontology simply because it is one of the best known. We could have also selected another upper-level system such as PROTON [3] or Cyc [4] or one of the many with narrower concept or subject coverage.

SUMO is one of the formal ontologies that has been mapped to the WordNet lexicon, which adds to its semantic richness. SUMO is written in the SUO-KIF language. SUMO is free and owned by the IEEE. The ontologies that extend SUMO are available under GNU General Public License.

The abstract, conceptual organization of SUMO is shown by this diagram, which also points to its related MILO (MId-Level Ontology), which is being developed as a bridge between the abstract content of the SUMO and the richer detail of various domain ontologies:

At this level, the structure is quite abstract. But one can easily browse the SUMO structure. A nifty tool to do so is the KSMSA (Knowledge Support for Modeling and Simulation) ontology browser. Using a hierarchical tree representation, you can navigate through SUMO, MILO, WordNet, and (with the locally installed version) Wikipedia.

The figure below shows the upper-level entity concept on the left; the right-hand panel shows a drill-down into the example atom entity:

Example SUMO Categories
[Click on image for full-size pop-up]

These views may be a bit misleading because the actual underlying structure, while it has hierarchical aspects as shown here, really is in the form of a directed acyclic graph (showing other relatedness options, not just hierarchical ones). So, alternate visualizations include traditional network graphs.

The other thing to note is that the “things” covered in the ontology, the entities, are also fairly abstract. That is because the intention of a standard “upper-level” ontology is to cover all relevant knowledge aspects of each entity’s domain. This approach results in a subject and topic coverage that feels less “concrete” than the coverage in, say, an encyclopedia, directory or card catalog.

Ontology Binding and Integration Mechanisms

According to Park and Durusau, upper ontologies are diverse, middle ontologies are even more diverse, and lower ontologies are more diverse still. A key observation is that ontological diversity is a given and increases as we approach real user interaction levels. Moreover, because of the “loose” nature of ontologies on the Web (now and into the future), diversity of approach is a further key factor.

Recall the initial discussion on the role and objectives of ontologies. About half of those roles involve effectively accessing or querying more than one ontology. The objective of “upper-level” ontologies, many with their own binding layers, is also expressly geared to ontology integration or federation. So, what are the possible mechanisms for such binding or integration?

A fundamental distinction within mechanisms to combine ontologies is whether it is a unified or centralized approach (often imposed or required by some party) or whether it is a schema mapping or binding approach. We can term this distinction centralized v. federated.

Centralized Approaches

Centralized approaches can take a number of forms. At the most extreme, adherence to a centralized approach can be contractual. At the other end are reference models or standards. For example, illustrative reference models include:

  • the Data Reference Model (DRM), one of the five reference models of the Federal Enterprise Architecture (FEA)
  • UDEF (Unified Data Element Framework), an approach toward a unified description framework, or
  • the eXtended MetaData Registry (XMDR) project.

Though I have argued that One Ring to Rule them All is not appropriate to the general Web, there may be cases within certain enterprises or where through funding clout (such as government contracts), some form of centralized approach could be imposed [5]. And, frankly, even where compliance can not be assured, there are advantages in economy, efficiency and interoperability to attempt central ontologies. Certain industries — notably pharmaceuticals and petrochemicals — and certain disciplines — such as some areas of biology among others — have through trade associations or community consensus done admirable jobs in adopting centralized approaches.

Federated Approaches

However, combining ontologies in the context of the broader Internet is more likely through federated approaches. (Though federated approaches can also be improved when there are consensual standards within specific communities.) The key aspect of a federated approach is to acknowledge that multiple schema need to be brought together, and that each contributing data set and its schema will not be altered directly and will likely remain in place.

Thus, the key distinctions within this category are the mechanisms by which those linkages may take place An important goal in any federated approach is to achieve interoperability at the data or instance level without unacceptable loss of information or corruption of the semantics. Numerous specific approaches are possible, but three example areas in RDF-topic map interoperability, the use of “subject maps”, and binding layers can illustrate some of the issues at hand.

In 2006 the W3C set up a working group to look at the issue of RDF and topic maps interoperability. Topic maps have been embraced by the library and information architecture community for some time, and have standards that have been adopted under ISO. Somewhat later but also in parallel was the development of the RDF standard by W3C. The interesting thing was that the conceptual underpinnings and objectives between these two efforts were quite similar. Also, because of the substantive thrust of topic maps and the substantive needs of its community, quite a few topic maps had been developed and implemented.

One of the first efforts of the W3C work group was to evaluate and compare five or six extant proposals for how to relate RDF and topic maps [6]. That report is very interesting reading for any one desirous of learning more about specific issues in combining ontologies and their interoperability. The result of that evaluation then led to some guidelines for best practices in how to complete this mapping [7]. Evaluations such as these provide confidence that interoperability can be achieved between relatively formal schema definitions without unacceptable loss in meaning.

A different, “looser” approach, but one which also grew out of the topic map community, is the idea of “subject maps.” This effort, backed by Park and Durusau noted above, but also with the support of other topic map experts such as Steve Newcomb and Robert Barta via their proposed Topic Maps Reference Model (ISO 13250-5), seems to be one of the best attempts I’ve seen that both respects the reality of the actual Web while proposing a workable, effective scheme for federation.

The basic idea of a subject map is built around a set of subject “proxies.” A subject proxy is a computer representation of a subject that can be implemented as an object, must have an identity, and must be addressable (this point provides the URI connector to RDF). Each contributing schema thus defines its own subjects, with the mappings becoming meta-objects. These, in turn, would benefit from having some accepted subject reference schema (not specifically addressed by the proponents) to reduce the breadth of the ultimate mapped proxy “space.”

I don’t have the expertise to judge further the specifics, but I find the presentation and papers by Park and Durusau, Avoiding Hobson’s Choice In Choosing An Ontology and Towards Subject-centric Merging of Ontologies to be worthwhile reading in any case. I highly recommend these papers for further background and clarity.

As the third example, “binding layers” are a comparatively newer concept. Leading upper-level ontologies such as SUMO or PROTON propose their own binding protocols to their “lower” domains, but that approach takes place within the construct of the parent upper ontology and language. Such designs are not yet generalized solutions. By far the most promising generalized binding solution is the SKOS (Simple Knowledge Organization System). Because of its importance, the next section is devoted to it.

Finally, with respect to federated approaches, there are quite a few software tools that have been developed to aid or promote some of these specific methods. For, example, about twenty of the software applications in my Sweet Tools listing of 500+ semantic Web and -related tools could be interpreted as aiding ontology mapping or creation. You may want to check out some of these specific tools depending on your preferred approach [8].

The Role of SKOS – the Simple Knowledge Organization System

SKOS, or the Simple Knowledge Organization System, is a formal language and schema designed to represent such structured information domains as thesauri, classification schemes, taxonomies, subject-heading systems, controlled vocabularies, or others; in short, most all of the “loosely defined” ontology approaches discussed herein. It is a W3C initiative more fully defined in its SKOS Core Guide [9].

SKOS is built upon the RDF data model of the subject-predicate-object “triple.” The subjects and objects are akin to nouns, the predicate a verb, in a simple Dick-sees-Jane sentence. Subjects and predicates by convention are related to a URI that provides the definitive reference to the item. Objects may be either a URI resource or a literal (in which case it might be some indexed text, an actual image, number to be used in a calculation, etc.).

Being an RDF Schema simply means that SKOS adds some language and defined relationships to this RDF baseline. This is a bit of recursive understanding, since RDFS is itself defined in RDF by virtue of adding some controlled vocabulary and relations. The power, though, is that these schema additions are also easily expressed and referenced.

This RDFS combination can thus be shown as a standard RDF triple graph, but with the addition of the extended vocabulary and relations:

Standard RDF Graph Model

The power of the approach arises from the ability of the triple to express virtually any concept, further extended via the RDFS language defined for SKOS. SKOS includes concepts such as “broader” and “narrower”, which enable hierarchical relations to be modeled, as well as “related” and “member” to support networks and arrays, respectively [9].

We can visualize this transforming power by looking at how an “ontology” in a totally foreign scheme can be related to the canonical SKOS scheme. In the figure below the left-hand portion shows the native hierarchical taxonomy structure of the UK Archival Thesaurus (UKAT), next as converted to SKOS on the right (with the overlap of categories shown in dark purple). Note the hierarchical relationships visualize better via a taxonomy, but that the RDF graph model used by SKOS allows a richer set of additional relationships including related and alternative names:

Example Structural Comparison of Hierarchical Taxonomy with Network Graph
[Click on image for full-size pop-up]

SKOS also has a rich set of annotation and labeling properties to enhance human readability of schema developed in it [9]. There is also a useful draft schema that the W3C’s SWEO (Semantic Web Education and Outreach) group is developing to organize semantic Web-related information [10].

Combined, these constructs provide powerful mechanisms for giving contributory ontologies a common conceptualization. When added to other sibling RDF schema such as FOAF or SIOC or DOAP, still additional concepts can be collated.

Conclusions

While not addressed directly in this piece, it is obviously of first importance to have content with structure before the questions of connecting that information can even arise. Then, that structure must also be available in a form suitable for merging or connection.

At that point, the subjects of this posting come into play.

We are stubbing our toes on the rocks while we gaze at the heavens.

We see that the daily Web has a diversity of schema or ontologies “loosely defined” for representing the structure of the content. These representations can be transferred to more complex schema, but not in the opposite direction. Moreover, the semantic basis for how to make these mappings also needs some common referents.

RDF provides the canonical data model for the data transfers and representations. RDFS, especially in the form of SKOS, appears to form one basis for the syntax and language for these transformations. And SKOS, with other schema, also appears to offer much of the appropriate “middle ground” for data relationships mapping.

However, lacking in this story is a referential structure for subject relationships [11]. (Also lacking are the ultimately critical domain specifics required for actual implementation.)

Abstract concepts of interest to philosophers and deep thinkers have been given much attention. Sadly, to date, concrete subject structures in which tangible things and tangible actions can be shared, is still very, very weak. We are stubbing our toes on the rocks while we gaze at the heavens.

Yet, despite this, simple and powerful infrastructures are well in-hand to address all foreseeable syntactic and semantic issues. There appear to be no substantive limits to needed next steps.

Lastly, many valuable resources for further reading and learning may be found within the Ontolog Community, W3C, TagCommons and Topics Maps groups. Enjoy! And be wary of ontology no longer.


[1] Deborah L. McGuinness. “Ontologies Come of Age”. In Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, editors. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2003. See http://www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-mit-press-(with-citation).htm
[2] I think it would be much clearer to refer to “upper level” ontologies as abstract or conceptual, “mid levels” as mapping or binding, and “lower levels” as domain (without any hierarchical distinctions such as lower or lowest or sub-domain), but current practice is probably too entrenched to change now.
[3] There are many aspects that make PROTON one of the more attractive reference ontologies. The PROTON ontology (PROTo ONtology), developed within the scope of the SEKT project, is attractive because of its understandability, relatively small size, modular architecture and a simple subsumption hierarchy. It is available in an OWL Lite form and is easy to adopt and extend. On the face of it, the Topic class within PROTON, which is meant to serve as a bridge between different ontologies, may also provide a binding layer to specific subject topics as sub-classes or class instances.
[5] Even with such clout, it is questionable to get rather complete adherence, as Ada showed within the Federal government. However, where circumstances allow it, central schema and ontologies may be worth pursuing because of improved interoperability and lower costs, even where some portions do not adhere and are more chaotic like the standard Web.
[6] See, A Survey of RDF/Topic Maps Interoperability Proposals, W3C Working Group Note 10 February 2006, Pepper, Vitali, Garshol, Gessa, Presutti (eds.)
[7] See, Guidelines for RDF/Topic Maps Interoperability, W3C Editor’s Draft 30 June 2006, Pepper, Presutti, Garshol, Vitali (eds.)
[8] Here are some Sweet Tools that may have a usefulness to ontology federation and creation:
  • Adaptiva — is a user-centered ontology building environment, based on using multiple strategies to construct an ontology, minimising user input by using adaptive information extraction
  • Altova SemanticWorks — is a visual RDF and OWL editor that auto-generates RDF/XML or nTriples based on visual ontology design
  • CMS — the 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
  • ConcepTool — is a system to model, analyze, verify, validate, share, combine, and reuse domain knowledge bases and ontologies, reasoning about their implication
  • ConRef — is a service discovery system which uses ontology mapping techniques to support different user vocabularies
  • FOAM — is the Framework for Ontology Alignment and Mapping. It is based on heuristics (similarity) of the individual entities (concepts, relations, and instances)
  • 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
  • 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
  • IODT — is IBM’s toolkit for ontology-driven development. The toolkit includes EMF Ontology Definition Metamodel (EODM), EODM workbench, and an OWL Ontology Repository (named Minerva)
  • KAON — is an open-source ontology management infrastructure targeted for business applications. It includes a comprehensive tool suite allowing easy ontology creation and management and provides a framework for building ontology-based applications. An important focus of KAON is scalable and efficient reasoning with ontologies
  • LinKFactory — is Language & Computing’s ontology management tool. It provides an effective and user-friendly way to create, maintain and extend extensive multilingual terminology systems and ontologies (English, Spanish, French, etc.). It is designed to build, manage and maintain large, complex, language independent ontologies
  • M3t4.Studio Semantic Toolkit — is Metatomix’s free set of Eclipse plug-ins to allow developers to create and manage OWL ontologies and RDF documents
  • MAFRA Toolkit — the Ontology MApping FRAmework Toolkit allows to create semantic relations between two (source and target) ontologies, and apply such relations in translating source ontology instances into target ontology instances
  • 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.”
  • OntoPortal — enables the authoring and navigation of large semantically-powered portals
  • OWLS-MX — the 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
  • pOWL — is a semantic Web development platform for ontologies in PHP. pOWL consists of a number of components, including RAP
  • Protege — is an open source visual ontology editor written in Java with many plug-in tools
  • Semantic Net Generator — is a utility for generating topic maps automatically from different data sources by using rules definitions specified with Jelly XML syntax. This Java library provides Jelly tags to access and modify data sources (also RDF) to create a semantic network
  • SOFA — is a Java API for modeling ontologies and Knowledge Bases in ontology and Semantic Web applications. It provides a simple, abstract and language neutral ontology object model, inferencing mechanism and representation of the model with OWL, DAML+OIL and RDFS languages
  • Terminator — is a tool for creating term to ontology resource mappings (documentation in Finnish)
  • WebOnto — supports the browsing, creation and editing of ontologies through coarse grained and fine grained visualizations and direct manipulation.
[9] The SKOS language has the following classes:
  • CollectableProperty — A property which can be used with a skos:Collection
  • Collection — A meaningful collection of concepts
  • Concept — An abstract idea or notion; a unit of thought
  • ConceptScheme — A set of concepts, optionally including statements about semantic relationships between those concepts. Thesauri, classification schemes, subject heading lists, taxonomies, ‘folksonomies’, and other types of controlled vocabulary are all examples of concept schemes. Concept schemes are also embedded in glossaries and terminologies.
  • OrderedCollection — An ordered collection of concepts, where both the grouping and the ordering are meaningful
. . . and the following properties:
  • altLabel — An alternative lexical label for a resource. Acronyms, abbreviations, spelling variants, and irregular plural/singular forms may be included among the alternative labels for a concept
  • altSymbol — An alternative symbolic label for a resource
  • broader — A concept that is more general in meaning. Broader concepts are typically rendered as parents in a concept hierarchy (tree)
  • changeNote — A note about a modification to a concept
  • definition — A statement or formal explanation of the meaning of a concept
  • editorialNote — A note for an editor, translator or maintainer of the vocabulary
  • example — An example of the use of a concept
  • hasTopConcept — A top level concept in the concept scheme
  • hiddenLabel — A lexical label for a resource that should be hidden when generating visual displays of the resource, but should still be accessible to free text search operations
  • historyNote — A note about the past state/use/meaning of a concept
  • inScheme — A concept scheme in which the concept is included. A concept may be a member of more than one concept scheme
  • isPrimarySubjectOf — A resource for which the concept is the primary subject
  • isSubjectOf –A resource for which the concept is a subject
  • member — A member of a collection
  • memberList — An RDF list containing the members of an ordered collection
  • narrower — A concept that is more specific in meaning. Narrower concepts are typically rendered as children in a concept hierarchy (tree)
  • note — A general note, for any purpose. The other human-readable properties of definition, scopeNote, example, historyNote, editorialNote and changeNote are all sub-properties of note
  • prefLabel — The preferred lexical label for a resource, in a given language. No two concepts in the same concept scheme may have the same value for skos:prefLabel in a given language
  • prefSymbol — The preferred symbolic label for a resource
  • primarySubject — A concept that is the primary subject of the resource. A resource may have only one primary subject per concept scheme
  • related — A concept with which there is an associative semantic relationship
  • scopeNote — A note that helps to clarify the meaning of a concept
  • semanticRelation — A concept related by meaning. This property should not be used directly, but as a super-property for all properties denoting a relationship of meaning between concepts
  • subject — A concept that is a subject of the resource
  • subjectIndicator — A subject indicator for a concept. [The notion of 'subject indicator' is defined here with reference to the latest definition endorsed by the OASIS Published Subjects Technical Committee]
  • symbol — An image that is a symbolic label for the resource. This property is roughly analagous to rdfs:label, but for labelling resources with images that have retrievable representations, rather than RDF literals. Symbolic labelling means labelling a concept with an image.
[10] The SWEO classification ontology is still under active development and has these draft classes. Note, however, the relative lack of actual subject or topic matter:
Classes are currently defined as:
  • article – magazine article
  • blog – blog discussing SW topics
  • book – indicates a textbook, applies to the book’s home page, review or listing in Amazon or such
  • casestudy – Article on a business case
  • conference/event – conferences or events where you can learn about the Semantic Web
  • demo/demonstration – interactive SW demo
  • forum – a forum on semantic web or related topics
  • presentation – Powerpoint or similar slide show
  • person – If this is a person’s home page or blog, see below
  • publication – a scientific publication
  • ontology – a formalisation of a shared conceptualization using OWL, RDFS, SKOS or something else based on RDF
  • organization – If the page is the home page of an organization, research, vendor etc, see below
  • portal – a portal website Semantic Web or related topics, usually hosting information items, mailinglists, community tools
  • project – a research (for example EU-IST) or other project that addresses Semantic Web issues
  • mailinglist – a mailinglist on semantic Web or related topics
  • person – ideally a person that is well known regarding the Semantic Web (people who can do keynote speakers), may also be any related person
  • press – a press release by a company or an article about Semantic Web
  • recommended – If the resource is seen to be in the top 10 of its kind
  • specification – a Semantic Web specification (RDF, RDF/S, OWL, etc)
  • categories – (perhaps using tags or other free form annotation
  • successstory – Article that can contain advertisment and clearly shows the benefit of semantic web
  • tutorial – a tutorial teaching some aspect of semantic web, an example
  • vocabulary – a RDF vocabulary
  • software project/tool – For product/project home pages
If the page describes an organization, it can be tagged as:
  • vendor
  • research
  • enduser
If the page is a person’s home page or blog or similar, it could be:
  • opinionleader
  • researcher
  • journalist
  • executive
  • geek
The type of audience can also be tagged, for example:
  • general public
  • beginners
  • technicians
  • researchers.
[11] The OASIS Topic Maps Published Subjects Technical Committee was formed a number of years back to promote Topic Maps interoperability through the use of Published Subjects Indicators (PSIs). Their resulting report was a very interesting effort that unfortunately did not lead to wide adoption, perhaps because the effort was a bit ahead of its time or it was in advance of the broader acceptance of RDF. This general topic is the subject of a later posting by me.
[12] See further, Leo Obrst, “The Semantic Spectrum & Semantic Models,” a Powerpoint presentation (http://ontolog.cim3.net/file/resource/presentation/LeoObrst_20060112/OntologySpectrumSemanticModels–LeoObrst_20060112.ppt)
made as part of an Ontolog Forum (http://ontolog.cim3.net/) presentation in two parts, “What is an Ontology? – A Briefing on the Range of Semantic Models” (see http://ontolog.cim3.net/cgi-bin/wiki.pl?ConferenceCall_2006_01_12), in January 2006. Leo Obrst is a principal artificial intelligence scientist at MITRE’s (http://www.mitre.org) Center for Innovative Computing and Informatics and a co-convener of the Ontolog Forum. His presentation is a rich source of practical overview information on ontologies.
[13] The actual diagram is an unattributed modification by Dan McCreary (see http://www.danmccreary.com/presentations/sem_int/sem_int.ppt) based on Obrst’s material in [12].
Posted:November 22, 2010

Horse by RuthThe Reality is: Most Connections are Proximate

What does it mean to interoperate information on the Web? With linked data and other structured data now in abundance, why don’t we see more information effectively combined? Why express your information as linked data if no one is going to use it?

Interoperability comes down to the nature of things and how we describe those things or quite similar things from different sources. This was the major thrust of my recent keynote presentation to the Dublin Core annual conference. In that talk I described two aspects of the semantic “gap”:

  1. One aspect is the need for vetted reference sources that provide the entities and concepts for aligning disparate content sources on the Web, and
  2. A second aspect is the need for accurate mapping predicates that can represent the often approximate matches and overlaps of this heterogeneous content.

I’ll discuss the first “gap” in a later post. What we’ll discuss here is the fact that most relationships between putatively same things on the Web are rarely exact, and are most often approximate in nature.

“It Ain’t the Label, Mabel”

The use of labels for matching or descriptive purposes was the accepted practice in early libraries and library science. However, with the move to electronic records and machine bases for matching, appreciation for ambiguities and semantics have come to the fore. Labels are no longer an adequate — let alone a sufficient — basis for matching references.

The ambiguity point is pretty straightforward.  Refer to Jimmy Johnson by his name, and you might be referring to a former football coach, a NASCAR driver, a former boxing champ, a blues guitarist, or perhaps even a plumber in your home town. Or perhaps none of these individuals. Clearly, the label “Jimmy Johnson” is insufficient to establish identity.

Of course, not all things are named entities such as a person’s name. Some are general things or concepts. But, here, semantic heterogeneities can also lead to confusion and mismatches. It is always helpful to revisit the sources and classification of semantic heterogeneities, which I first discussed at length nearly five years ago. Here is a schema classifying more than 40 categories of potential semantic mismatches [1]:

Class Category Subcategory
STRUCTURAL Naming Case Sensitivity
Synonyms
Acronyms
Homonyms
Generalization / Specialization
Aggregation Intra-aggregation
Inter-aggregation
Internal Path Discrepancy
Missing Item Content Discrepancy
Attribute List Discrepancy
Missing Attribute
Missing Content
Element Ordering
Constraint Mismatch
Type Mismatch
DOMAIN Schematic Discrepancy Element-value to Element-label Mapping
Attribute-value to Element-label Mapping
Element-value to Attribute-label Mapping
Attribute-value to Attribute-label Mapping
Scale or Units
Precision
Data Representation Primitive Data Type
Data Format
DATA Naming Case Sensitivity
Synonyms
Acronyms
Homonyms
ID Mismatch or Missing ID
Missing Data
Incorrect Spelling
LANGUAGE Encoding Ingest Encoding Mismatch
Ingest Encoding Lacking
Query Encoding Mismatch
Query Encoding Lacking
Languages Script Mismatches
Parsing / Morphological Analysis Errors (many)
Syntactical Errors (many)
Semantic Errors (many)

Even with the same label, two items in different information sources can refer generally to the same thing, but may not be the same thing or may define it with a different scope and content. In broad terms, these mismatches can be due to structure, domain, data or language, with many nuances within each type.

The sameAs approach used by many of the inter-dataset linkages in linked data ignores these heterogeneities. In a machine and reasoning sense, indeed even in a linking sense, these assertions can make as little or nonsensical sense as talking about the plumber with the facts about the blues guitarist.

Cats, Paul Newman and Great Britain

Let’s take three examples where putatively we are talking about the same thing and linking disparate sources on the Web.Great Britain Usages

The first example is the seemingly simple idea of “cats”. In one source, the focus might be on house cats, in another domestic cats, and in a third, cats as pets. Are these ideas the same thing? Now, let’s bring in some taxonomic information about the cat family, the Felidae. Now, the idea of “cats” includes lynx, tigers, lions, cougars and many other kinds of cats, domestic and wild (and, also extinct!). Clearly, the “cat” label used alone fails us miserably here.

Another example is one that Fred Giasson and I brought up one year ago in When Linked Data Rules Fail [2]. That piece discussed many poor practices within linked data, and used as one case the treatment of articles in the New York Times about the (deceased) actor Paul Newman. The NYT dataset is about various articles written about people historically in the newspaper. Their record about Paul Newman was about their pool of articles with attributes such as first published and so forth, with no direct attribute information about Paul Newman the person. Then, they asserted a sameAs relationship with external records in Freebase and DBpedia, which acts to commingle person attributes like birth, death and marriage with article attributes such as first and last published. Clearly, the NYT has confused the topic ( Paul Newman) of a record with the nature of that record (articles about topics). This misunderstanding of the “thing” at hand makes the entailed assertions from the multiple sources illogical and useless [3].

Our third example is the concept or idea or named entity of Great Britain. Depending on usage and context, Great Britain can refer to quite different scopes and things. In one sense, Great Britain is an island. In a political sense, Great Britain can comprise the territory of England, Scotland and Wales. But, even more precise understandings of that political grouping may include a number of outlying islands such as the Isle of Wight, Anglesey, the Isles of Scilly, the Hebrides, and the island groups of Orkney and Shetland. Sometimes the Isle of Man and the Channel Islands, which are not part of the United Kingdom, are fallaciously included in that political grouping. And, then, in a sporting context, Great Britain may also include Northern Ireland. Clearly, these, plus other confusions, can mean quite different things when referring to “Great Britain.” So, without definition, a seemingly simple question such as what the population of Great Britain is could legitimately return quite disparate values (not to mention the time dimension and how that has changed boundaries as well!).

These cases are quite usual for what “things” mean when provided from different sources with different perspectives and with different contexts. If we are to get meaningful interoperation or linkage of these things, we clearly need some different linking predicates.

Some Attempts at ‘Approximateness’

The realization that many connections across datasets on the Web need to be “approximate” is growing. Here is the result of an informal survey for leading predicates in this regard [4]:

  • skos:broadMatch
  • skos:related
  • ore:similarTo
  • dul:associatedWith
  • umbel:isAbout
  • skos:narrowMatch
  • vmf:isInVocabulary
  • skos:closeMatch
  • owl:equivalentClass
  • skos:mappingRelation
  • ov:similarTo
  • umbel:hasMapping
  • doape:similarThing
  • lvont:nearlySameAs
  • umbel:isRelatedTo
  • umbel:isLike
  • skos:exactMatch
  • sswap:hasMapping
  • umbel:hasCharacteristic
  • lvont:somewhatSameAs
  • dul:isAbout
  • skos:semanticRelation
  • rdfs:seeAlso
  • ore:describes
  • skos:narrowerTransitive
  • map:narrowerThan
  • dul:isConceptualizedBy
  • skos:narrower
  • umbel:isCharacteristicOf
  • prowl:defineUncertaintyOf
  • dc:subject
  • sumo:entails
  • link:uri
  • foaf:isPrimaryTopicOf
  • skos:broaderTransitive
  • dul:isComponentOf
  • foaf:focus
  • skos:relatedMatch
  • map:broaderThan
  • owl:sameAs
  • skos:broader
  • dul:isAssignedTo
  • wn:similarTo
  • sumo:refers
  • rdfs:subClassOf

Besides the standard OWL and RDFS predicates, SKOS, UMBEL and DOLCE [5] provide the largest number of choices above. In combination, these predicates probably provide a good scoping of “approximateness” in mappings.

Rationality and Reasoners

It is time for some leadership to emerge to provide a more canonical set of linking predicates for these real-world connection requirements. It would also be extremely useful to have such a canonical set adopted by some leading reasoners such that useful work could be done against these properties.


[1] See M. K. Bergman, 2006. “Sources and Classification of Semantic Heterogeneities,” AI3:::Adaptive Information blog, June 6, 2006. See http://www.mkbergman.com/232/sources-and-classification-of-semantic-heterogeneities/.
[2] See M. K. Bergman and F. Giasson, 2009. “When Linked Data Rules Fail,” AI3:::Adaptive Information blog, November 16, 2009. See http://www.mkbergman.com/846/when-linked-data-rules-fail/.
[3] On a different disappointing note, the critical errors that we noted a year ago and the NYT’s own acknowledgement on its site that:
“An RDFS description and English language documentation for the NYT namespace will be provided soon. Thanks for your patience.”
has still not been corrected, now a year later. Poor performance like this by a professional publisher gives linked data a bad name.
[4] These predicates have been obtained from personal knowledge and directed searches using the Falcons ontology search service. Simple Web searches on the namespace plus predicate name will provide more detail on any given predicate.
[5] UMBEL (Upper Mapping and Binding Exchange Layer) is an ontology of about 20,000 subject concepts that acts as a reference structure for inter-relating disparate datasets. It is also a general vocabulary of classes and predicates designed for the creation of domain-specific ontologies. For SKOS, see Alistair Miles and Sean Bechhofer, eds., 2009. SKOS Simple Knowledge Organization System Reference, W3C Recommendation, 18 August 2009; http://www.w3.org/TR/skos-reference/. The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is one of the more popular upper ontologies.
Posted:July 6, 2010

Consolidating Under the Open Semantic Framework
Release of Semantic Components Adds Final Layer, Leads to Streamlined Sites

Yesterday Fred Giasson announced the release of code associated with Structured Dynamics‘ open source semantics components (also called sComponents).  A semantic component is an ontology-driven component, or widget, based on Flex. Such a component takes record descriptions, ontologies and target attributes/types as inputs and then outputs some (possibly interactive) visualizations of the records.

Though not all layers are by any means complete, from an architectural standpoint the release of these semantic components provides the last and missing layer to complete our open semantic framework. Completing this layer now also enables Structured Dynamics to rationalize its open source Web sites and various groups and mailing lists associated with them.

The OSF “Semantic Muffin”

We first announced the open semantic framework — or OSF — a couple of weeks back. Refer to that original post for more description of the general design [1]. However, we can show this framework with the semantic components layer as illustrated by what some have called the “semantic muffin”:

Incremental Layers of the Open Semantic Framework

(click for full size)

The OSF stack consists of these layers, moving from existing assets upward through increasing semantics and usability:

  • Existing assets — any and all existing information and data assets, ranging from unstructured to structured. Preserving and leveraging those assets is a key premise
  • scones / irON — this layer is for general conversion of non-RDF data and data schema to RDF (via irON or RDFizers) or for information extraction of subject concepts or named entities (scones)
  • structWSF — is the pivotal Web services framework layer, and provides the standard, common interface by which existing information assets get represented and presented to the outside world and to other layers in the OSF stack
  • Semantic components — the highlighted layer in the “semantic muffin”; in essence, this is the visualization and data interaction layer in the OSF stack; see more below
  • Ontologies — are the layer containing the structured assets “driving” the system; this includes the concepts and relationships of the domain at hand, and administrative ontologies that guide how the user interfaces or widgets in the system should behave, and
  • conStruct — is the content management system (CMS) layer based on Drupal and the thinnest layer with respect to OSF; this optional layer provides the theming, user rights and permissions, or other functionality drawn from Drupal’s 6500 third-party modules.

Not all of these layers are required in a given deployment and their adoption need not be sequential or absolutely depend on prior layers. Nonetheless, they do layer and interact with one another in the general manner shown.

The Semantics Components Layer

Current semantic components, or widgets, include: filter; tabular templates (similar to infoboxes); maps; bar, pie or linear charts; relationship (concept) browser; story and text annotator and viewer; workbench for creating structured views; and dashboard for presenting pre-defined views and component arrangements. These are generic tools that respond to the structures and data fed to them, adaptable to any domain without modification.

Though Fred’s post goes into more detail — with subsequent posts to get into the technical nuances of the semantic components — the main idea of these components is shown by the diagram below.

These various semantic components get embedded in a layout canvas for the Web page. By interacting with the various components, new queries are generated (most often as SPARQL queries) to the various structWSF Web services endpoints. The result of these requests is to generate a structured results set, which includes various types and attributes.

An internal ontology that embodies the desired behavior and display options (SCO, the Semantic Component Ontology) is matched with these types and attributes to generate the formal instructions to the semantic components. These instructions are presented via the sControl component, that determines which widgets (individual components, with multiples possible depending on the inputs) need to be invoked and displayed on the layout canvas. Here is a picture of the general workflow:

Semantic Components Workflow

(click for full size)

New interactions with the resulting displays and components cause the iteration path to be generated anew, again starting a new cycle of queries and results sets. As these pathways and associated display components get created, they can be named and made persistent for later re-use or within dashboard invocations.

Consolidating and Rationalizing Web Sites and Mailing Lists

OpenStructs and Open Semantic Framework LogoAs the release of the semantic components drew near, it was apparent that releases of previous layers had led to some fragmentation of Web sites and mailing lists. The umbrella nature of the open semantic framework enabled us to consolidate and rationalize these resources.

Our first change was to consolidate all OSF-related material under the existing OpenStructs.org Web site. It already contained the links and background material to structWSF and irON. To that, we added the conStruct and OSF material as well. This consolidation also allowed us to retire the previous conStruct Web site as well, which now re-directs to OpenStructs.

We also had fragmentation in user groups and mailing lists. Besides shared materials, these had many shared members. The Google groups for irON, structWSF and conStruct were thus archived and re-directed to the new Open Semantic Framework Google group and mailing list. Personal notices of the change and invites have been issued to all members of the earlier groups. For those interested in development work and interchange with other developers on any of these OSF layers, please now direct your membership and attention to the OSF group.

There has also been a revigoration of the developers’ community Web site at http://community.openstructs.org/. It remains the location for all central developer resources, including bug and issue tracking and links to SVNs.

Actual code SVN repositories are unchanged. These code repositories may be found at:

We hope you find these consolidations helpful. And, of course, we welcome new participants and contributors!


[1] An alternative view of this layer diagram is shown by the general Structured Dynamics product stack and architecture.
Posted:June 11, 2010

How Shall We Measure Progress Over the Past Three Years?

Friday     Brown Bag Lunch
Colorado  Interstate construction - 1970; courtesy National ArchivesFor a dozen years, my career has been centered on Internet search, dynamic content and the deep Web. For the past few years, I have been somewhat obsessed by two topics.

The first topic, a conviction really, is that implicit structure needs to be extracted from Web content to enable it to be disambiguated, organized, shared and re-purposed. The second topic, more an open question as a former academic married to a professor, is what might replace editorial selections and peer review to establish the authoritativeness of content. These topics naturally steer one to the semantic Web.

A Millennial Perspective

The semantic Web, by whatever name it comes to be called, is an inevitability. History tells us that as information content grows, so do the mechanisms for organizing and managing it. Over human history, innovations such as writing systems, alphabetization, pagination, tables of contents, indexes, concordances, reference look-ups, classification systems, tables, figures, and statistics have emerged in parallel with content growth [19].

When the Lycos search engine, one of the first profitable Internet ventures, was publicly released in 1994, it indexed a mere 54,000 pages [1]. When Google wowed us with its page-ranking algorithm in 1998, it soon replaced my then favorite search engine, AltaVista. Now, tens of billions of indexed documents later, I often find Google’s results to be overwhelming dross — unfortunately true again for all of the major search engines. Faceted browsing, vertical search, and Web 2.0′s tagging and folksonomies demonstrate humanity’s natural penchant to fight this entropy, efforts that will next continue with the semantic Web and then mechanisms unforeseen to manage the chaos of burgeoning content.

An awful lot of hot air has been expelled over the false dichotomy of whether the semantic Web will fail or is on the verge of nirvana. Arguments extend from the epistemological versus ontological (classically defined) to Web 3.0 versus SemWeb or Web services (WS*) versus REST (Representational State Transfer). My RSS feed reader points to at least one such dust up every week.

Some set the difficulties of resolving semantic heterogeneities as absolutes, leading to an illogical and false rejection of semantic Web objectives. In contrast, some advocates set equally divisive arguments for semantic Web purity by insisting on formal ontologies and descriptive logics. Meanwhile, studied leaks about “stealth” semantic Web ventures mean you should grab your wallet while simultaneously shaking your head.

A Decades-Long Perspective

My mental image of the semantic Web is a road from here to some achievable destination — say, Detroit. Parts of the road are well paved; indeed, portions are already superhighways with controlled on-ramps and off-ramps. Other portions are two lanes, some with way too many traffic lights and some with dangerous intersections. A few small portions remain unpaved gravel and rough going.

1919 Wreck in Nebraska

Wreck in Nebraska during the 1919 Transcontinental Motor Convoy

A lack of perspective makes things appear either too close or too far away. The automobile isn’t yet a century old as a mass-produced item. It wasn’t until 1919 that the US Army Transcontinental Motor Convoy made the first automobile trip across the United States.

The 3,200 mile route roughly followed today’s Lincoln Highway, US 30, from Washington, D.C. to San Francisco. The convoy took 62 days and 250 recorded accidents to complete the trip (see figure), half on dirt roads at an average speed of 6 miles per hour. A tank officer on that trip later observed Germany’s autobahns during World War II. When he subsequently became President Dwight D. Eisenhower, he proposed and then signed the Interstate Highway Act.

That was 50 years ago. Today, the US is crisscrossed with 50,000 miles of interstates, which have completely remade the nation’s economy and culture [2].

Today’s Perspective

Like the interstate system in its early years, today’s semantic Web lets you link together a complete trip, but the going isn’t as smooth or as fast as it could be. Nevertheless, making the trip is doable and keeps improving day by day, month by month.

My view of what’s required to smooth the road begins with extracting structure and meaningful information according to understandable schema from mostly uncharacterized content. Then we store the now-structured content as RDF triples that can be further managed and manipulated at scale. By necessity, the journey embraces tools and requirements that, individually, might not constitute semantic Web technology as some strictly define it. These tools and requirements are nonetheless integral to reaching the destination. We are well into that journey’s first leg, what I and others are calling the structured Web.

For the past six months or so I have been researching and assembling as many semantic Web and related tools as I can find [3]. That Sweet Tools listing now exceeds 500 tools [4] (with its presentation using the nifty lightweight Exhibit publication system from MIT’s Simile program [5]). I’ve come to understand the importance of many ancillary tool sets to the entire semantic Web highway, such as natural language processing and information extraction. I’ve also found new categories of pragmatic tools that embody semantic Web and data mediation processes but don’t label themselves as such.

In its entirety, the Sweet Tools listing provides a pretty good picture of the semantic Web’s state. It’s a surprisingly robust picture — though with some notable potholes — and includes impressive open source options in all categories. Content publishing, indexing, and retrieval at massive scales are largely solved problems. We also have the infrastructure, languages, and (yes!) standards for tying this content together meaningfully at the data and object levels.

I also think a degree of consensus has emerged on RDF as the canonical data model for semantic information. RDF triple stores are rapidly improving toward industrial strength, and RESTful designs enable massive scalability, as terabyte- and petabyte-scale full-text indexes prove.

Powerful and flexible middleware options, such as those from OpenLink [6], can transform and integrate diverse file formats with a variety of back ends. The World Wide Web Consortium’s GRDDL standard [7] and related tools, plus various “RDF-izers” from Massachusetts Institute of Technology and elsewhere [8], largely provide the conversion infrastructure for getting Web data into that canonical RDF form. Sure, some of these converters are still research-grade, but getting them to operational capabilities at scale now appears trivial.

Things start getting shakier when trying to structure information into a semantic formalism. Controlled vocabularies and ontologies range broadly and remain a contentious area. Publishers and authors perhaps have too many choices: from straight Atom or RSS feeds and feeds with tags to informal folksonomies and then Outline Processor Markup Language [9] or microformats [10]. From there, the formalism increases further to include the standard RDF ontologies such as SIOC (Semantically-Interlinked Online Communities), SKOS (Simple Knowledge Organizing System), DOAP (Description of a Project), and FOAF (Friend of a Friend) [11] and the still greater formalism of OWL’s various dialects [12].

If we compare the semantic Web to the US interstate highway system, we’re still in the early stages of a journey that will remake our economy and culture.
Many potholes on the road to the semantic Web exist.
One ready task is to transform existing structure to RDF. Another priority is to refine tools to extract structure and meaningful information from uncharacterized content.

Arguing which of these is the theoretical best method is doomed to failure, except possibly in a bounded enterprise environment. We live in the real world, where multiple options will always have their advocates and their applications.

All of us should welcome whatever structure we can add to our information base, no matter where it comes from or how it’s done. The sooner we can embrace content in any of these formats and convert it into canonical RDF form, we can then move on to needed developments in semantic mediation, some of the roughest road on the journey.

Potholes on the Semantic Highway

Semantic mediation requires appropriate structured content. Many potholes on the road to the semantic Web exist because the content lacks structured markup; others arise because existing structure requires transformation. We need improved ways to address both problems. We also need more intuitive means for applying schema to structure. Some have referred to these issues as “who pays the tax.”

Recent experience with social software and collaboration proves that a portion of the Internet user community is willing to tag and characterize content. Furthermore, we can readily leverage that resulting structure, and free riders are welcomed. The real pothole is the lack of easy — even fun — data extractors and “structurizers.” But we’re tantalizingly close.

Tools such as Solvent and Sifter from MIT’s Simile program [13] and Marmite from Carnegie Mellon University [14] are showing the way to match DOM (document object model) inspectors with automated structure extractors. DBpedia, the alpha version of Freebase, and System One now provide large-scale, open Web data sets in RDF [15], including all of Wikipedia. Browser extensions such as Zotero [16] are showing how to integrate structure management into acceptable user interfaces, as are services such as Zoominfo [17]. Yet we still lack easy means to design the differing structures suitable for a plenitude of destinations.

Amazingly, a compelling road map for how all these pieces could truly fit together is also incomplete. How do we actually get from here to Detroit? Within specific components, architectural understandings are sometimes OK (although documentation is usually awful for open source projects, as most of the current tools are). Until our community better documents that vision, attracting new contributors will be needlessly slower, thus delaying the benefits of network effects.

So, let’s create a road map and get on with paving the gaps and filling the potholes. It’s not a matter of standards or technology — we have those in abundance. Let’s stop the silly squabbles and commit to the journey in earnest. The structured Web‘s ability to reach Hyperland [18], Douglas Adam’s prescient 1990 forecast of the semantic Web, now looks to be no further away than Detroit.

Friday      Brown Bag Lunch This Friday brown bag leftover was first placed into the AI3 refrigerator about three years ago on May 3, 2007.  The piece was my answer to a request by Jim Hendler to pen some thoughts on the semantic Web, based on I believe what he thought might be a pragmatic perspective combining Internet business with Web science. The formal piece appeared as a guest editorial in the May/June 2007 issue of IEEE Intelligent Systems. What appears above is unaltered from my original posting (aside from some minor formatting clean-up and — sorry to say — some of the projects are now defunct).

[1] Chris Sherman, “Happy Birthday, Lycos!,” Search Engine Watch, August 14, 2002. See http://searchenginewatch.com/showPage.html?page=2160551.
[2] David A. Pfeiffer, “Ike’s Interstates at 50: Anniversary of the Highway System Recalls Eisenhower’s Role as Catalyst,” Prologue Magazine, National Archives, Summer 2006, Vol. 38, No. 2. See: http://www.archives.gov/publications/prologue/2006/summer/interstates.html.
[3] The mention of specific tool names is meant to be illustrative and not necessarily a recommendation.
[6] OpenLink Software’s Virtuoso and Data Spaces products; see http://www.openlinksw.com/.
[7] W3C’s Gleaning Resource Descriptions from Dialects of Languages (GRDDL, pronounced “griddle”). See http://www.w3.org/2004/01/rdxh/spec.
[9] Outline Processor Markup Language (OPML); see http://www.opml.org/.
[10] Microformats; see http://microformats.org/.
[12] W3C’s Web Ontology Language (OWL). See http://www.w3.org/TR/owl-features/.
[13] Solvent (http://simile.mit.edu/wiki/Solvent) and Sifter (http://simile.mit.edu/wiki/Sifter) are from MIT’s Simile program.
[14] Marmite (http://www.cs.cmu.edu/~jasonh/projects/marmite/) is from Carnegie Mellon University.
[15] DBpedia (http://dbpedia.org/docs/) and Freebase (in alpha, by invitation only at http://www.freebase.com/) are two of the first large-scale open datasets on the Web; Wikipedia has also been converted to RDF by System One (http://labs.systemone.at/wikipedia3).
[16] Zotero is produced by George Mason University’s Center for History and New Media; see http://www.zotero.org.
[17] ZoomInfo (http://www.zoominfo.com/) provides online structured search of companies and people, plus broader services to enterprises.
[18] The late Douglas Adams, of Doctor Who and A Hitchhiker’s Guide to the Galaxy fame, produced a TV program for BBC2 presaging the Internet called Hyperland. This 50-min video can be seen in five parts via YouTube at Part 1 of 5, 2 of 5, 3 of 5, 4 of 5 and 5 of 5.
[19] Since I first wrote this piece, I have systematized these developments in my Timeline of Information History.