Posted:September 13, 2010

A Single-stop Assembly of Ontology Tips and Pointers

As we conclude this recent series on ontology tools and building [1], one item stands clear: the relative lack of guidance on how one actually builds and maintains these beasties. While there is much of a theoretic basis in the literature and on the Web, and much of methodologies and algorithms, there is surprisingly little on how one actually goes about creating an ontology.

An earlier posting pointed to the now classic Ontology Development 101 article as a good starting point [2]. Another really excellent starting point is the Protégé 4 user manual [3]. Though it is obviously geared to the Protégé tool and its interface, it also is an instructive tutorial on general ontology (OWL) topics and constructs. I highly recommend printing it out and reading it in full.

If you do nothing else, you should download, print and study in full the Protégé 4 users manual [3].

Learning by Example

Another way to learn more about ontology construction is to inspect some existing ontologies. Though one may use a variety of specialty search engines and Google to find ontologies [4], there are actually three curated services that are more useful and which I recommend.

The best, by far, is the repository created by the University of Manchester for the now-completed TONES project [5]. TONES has access to some 200+ vetted ontologies, plus a search and filtering facility that helps much in finding specific OWL constructs. It is a bit difficult to filter by OWL 2-compliant only ontologies (except for OWL 2 EL), but other than that, the access and use of the repository is very helpful. Another useful aspect is that the system is driven by the OWL API, a central feature that we recommended in the prior tools landscape posting. From a learning standpoint this site is helpful because you can filter by vocabulary.

An older, but similar, repository is OntoSelect. It is difficult to gauge how current this site is, but it nonetheless provides useful and filtered access to OWL ontologies as well.

These sources provide access to complete ontologies. Another way to learn about ontology construction is from a bottom-up perspective. In this regard, the Ontology Design Patterns (ODP) wiki is the definitive source [6]. This is certainly a more advanced resource, since its premise begins from the standpoint of modeling issues and patterns to address them, but the site is also backed by an active community and curated by leading academics. Besides ontology building patterns, ODP also has a listing of exemplary ontologies (though without the structural search and selection features of the sources above). ODP is not likely the first place to turn to and does not give “big picture” guidance, but it also should be a bookmarked reference once you begin real ontology development.

It is useful to start with fully constructed ontologies to begin to appreciate the scope involved with them. But, of course, how one gets to a full ontology is the real purpose of this post. For that purpose, let’s now turn our attention to general and then more specific best practices.

Sources of Best Practices

As noted above, there is a relative paucity of guidance or best practices regarding ontologies, their construction and their maintenance. However, that being said, there are some sources whereby guidance can be obtained.

To my knowledge, the most empirical listing of best practices comes from Simperl and Tempich [7]. In that 2006 paper they examined 34 ontology building efforts and commented on cost, effectiveness and methodology needs. It provides an organized listing of observed best practices, though much is also oriented to methodology. I think the items are still relevant, though they are now four to five years old. The paper also contains a good reference list.

Various collective ontology efforts also provide listings of principles or such, which also can be a source for general guidance. The OBO (The Open Biological and Biomedical Ontologies) effort, for example, provides a listing of principles to which its constituent ontologies should adhere [8]. As guidance to what it considers an exemplary ontology, the ODP effort also has a useful organized listing of criteria or guidance.

One common guidance is to re-use existing ontologies and vocabularies as much as possible. This is a major emphasis of the OBO effort [9]. The NeOn methodology also suggests guidelines for building individual ontologies by re-use and re-engineering of other domain ontologies or knowledge resources [10]. Brian Sletten (among a slate of emerging projects) has also pointed to the use of the Simple Knowledge Organization System (SKOS) as a staging vocabulary to represent concept schema like thesauri, taxonomies, controlled vocabularies, and subject headers [11].

The Protégé manual [3] is also a source of good tips, especially with regard to naming conventions and the use of the editor. Lastly, the major source for the best practices below comes from Structured Dynamics‘ own internal documentation, now permanently archived. We are pleased to now consolidate this information in one place and to make it public.

The best practices herein are presented as single bullet points. Not all are required and some may be changed depending on your own preferences. In all cases, however, these best practices are offered from Structured Dynamics’ perspective regarding the use and role of adaptive ontologies [12]. To our knowledge, this perspective is a unique combination of objectives and practices, though many of the individual practices are recommended by others.

General Best Practices

General best practices refer to how the ontology is scoped, designed and constructed. Note the governing perspective in this series has been on lightweight, domain ontologies.

Scope and Content

  • Provide balanced coverage of the subject domain. The breadth and depth of the coverage in the ontology should be roughly equivalent across its scope
  • Reuse structure and vocabularies as much as possible. This best practice refers to leveraging non-ontological content such as existing relational database schema, taxonomies, controlled vocabularies, MDM directories, industry specifications, and spreadsheets and informal lists. Practitioners within domains have been looking at the questions of relationships, structure, language and meaning for decades. Effort has already been expended to codify many of these understandings. Good practice therefore leverages these existing structural and vocabulary assets (of any nature), and relies on known design patterns
  • Embed the domain coverage into a proper context. A major strength of ontologies is their potential ability to interoperate with other ontologies. Re-using existing and well-accepted vocabularies and including concepts in the subject ontology that aid such connections is good practice. The ontology should also have sufficient reference structure for guiding the assignment of what content “is about”
  • Define clear predicates (also known as properties, relationships, attributes, edges or slots), including a precise definition. Then, when relating two things to one another, use care in actually assigning these properties. Initially, assignments should start with a logical taxonomic or categorization structure and expand from there into more nuanced predicates
  • Ensure the relationships in the ontology are coherent. The essence of coherence is that it is a state of logical, consistent connections, a logical framework for integrating diverse elements in an intelligent way. So while context supplies a reference structure, coherence means that the structure makes sense. Is the hip bone connected to the thigh bone, or is the skeleton askew? Testing (see below) is a major aspect for meeting this best practice
  • Map to external ontologies to increase the likelihood of sharing and interoperability. In Structured Dynamics’ case, we also attempt to map at minimum to the UMBEL subject reference structure for this purpose [13]
  • Rely upon a set of core ontologies for external re-use purposes; Structured Dynamics tends to rely on a set of primary and secondary standard ontologies [14]. The corollary to this best practice is don’t link indiscriminantly.

Structure and Design

  • Begin with a lightweight, domain ontology [15]. Ontologies built for the pragmatic purposes of setting context and aiding disparate data to interoperate tend to be lightweight with only a few predicates, such as isAbout, narrowerThan or broaderThan. But, if done properly, these lighter weight ontologies with more limited objectives can be surprisingly powerful in discovering connections and relationships. Moreover, they are a logical and doable intermediate step on the path to more demanding semantic analysis. Because we have this perspective, we also tend to rely heavily on the SKOS vocabulary for many of our ontology constructs [16]
  • Try to structurally split domain concepts from instance records. Concepts represent the nodes within the structure of the ontology (also known as classes, subject concepts or the TBox). Instances represent the data that populates that structure (also known as named entities, individuals or the ABox) [17]. Trying to keep the ABox and TBox separate enables easier maintenance, better understandability of the ontology, and better scalability and incorporation of new data repositories
  • Treat many concepts via “punning” as both classes and instances (that is, as either sets or members, depending on context). The “punning” technique enables “metamodeling,” such as treating something via its IRI as a set of members (such as Mammal being a set of all mammals) or as an instance (such as Endangered Mammal) when it is the object of a different contextual assertion. Use of “metamodeling” is often helpful to describe the overall conceptual structure of a domain. See endnote [18] for more discussion on this topic
  • Build ontologies incrementally. Because good ontologies embrace the open world approach [19], working toward these desired end states can also be incremental. Thus, in the face of common budget or deadline constraints, it is possible initially to scope domains as smaller or to provide less coverage in depth or to use a small set of predicates, all the while still achieving productive use of the ontology. Then, over time, the scope can be expanded incrementally. Much value can be realized by starting small, being simple, and emphasizing the pragmatic. It is OK to make those connections that are doable and defensible today, while delaying until later the full scope of semantic complexities associated with complete data alignment
  • Build modular ontologies that split your domain and problem space into logical clusters. Good ontology design, especially for larger projects, warrants a degree of modularity. An architecture of multiple ontologies often works together to isolate different work tasks so as to aid better ontology management. Also, try to use a core set of primitives to build up more complex parts. This is a kind of reuse within the same ontology, as opposed to reusing external ontologies and patterns. The corollary to this is: the same concepts are not created independently multiple times in different places in the ontology. Adhering to both of these practices tends to make ontology development akin to object-oriented programming
  • Assign domains and ranges to your properties. Domains apply to the subject (the left hand side of a triple); ranges to the object (the right hand side of the triple). Domains and ranges should not be understood as actual constraints, but as axioms to be used by reasoners. In general, domain for a property is the range for its inverse and the range for a property is the domain of its inverse. Use of domains and ranges will assist testing (see below) and help ensure the coherency of your ontology
  • Assign property restrictions, but do so sparingly and judiciously [20]. Use of property restrictions will assist testing (see below) and help ensure the coherency of your ontology
  • Use disjoint classes to separate classes from one another where the logic makes sense and dictates (if not explicitly stated, they are assumed to overlap)
  • Write the ontology in a machine-processable language such as OWL or RDF Schema (among others), and
  • Aggressively use annotation properties (see next) to promote the usefulness and human readability of the ontology.

Naming and Vocabulary Best Practices

  • Name all concepts as single nouns. Use CamelCase notation for these classes (that is, class names should start with a capital letter and not contain any spaces, such as MyNewConcept)
  • Name all properties as verb senses (so that triples may be actually read); e.g., hasProperty. Try to use mixedCase notation for naming these predicates (that is, begin with lower case but still capitalize thereafter and don’t use spaces)
  • Try to use common and descriptive prefixes and suffixes for related properties or classes (while they are just labels and their names have no inherent semantic meaning, it is still a useful way for humans to cluster and understand your vocabularies). For examples, properties about languages or tools might contain suffixes such as ‘Language‘ or ‘Tool‘ for all related properties
  • Provide inverse properties where it makes sense, and adjust the verb senses in the predicates to accommodate. For example, <Father> <hasChild> <Janie> would be expressed inversely as <Janie> <isChildOf> <Father>
  • Give all concepts and properties a definition. The matching and alignment of things is done on the basis of concepts (not simply labels) which means each concept must be defined [21]. Providing clear definitions (along with the coherency of its structure) gives an ontology its semantics. Remember not to confuse the label for a concept with its meaning. (This approach also aids multi-linguality). In its own ontologies, Structured Dynamics uses the property of skos:definition, though others such as rdfs:comment or dc:description are also commonly used
  • Provide a preferred label annotation property that is used for human readable purposes and in user interfaces. For this purpose, Structured Dynamics uses the property of skos:prefLabel
  • Include a class “SemSet”, which means a series of alternate labels and terms to describe the concept. These alternatives include true synonyms, but may also be more expansive and include jargon, slang, acronyms or alternative terms that usage suggests refers to the same concept. The umbel:SemSet construct enables a listing of individual members to be generated that provides the matching set for tagging and information extraction tasks. (As such, also include the prefLabel in the SemSet for proper lookup and tagging purposes.) The SemSet construct is similar to the “synsets” in Wordnet, but with a broader use understanding. This construct is an integral part of Structured Dynamics’ approach to using ontologies for information extraction and tagging of unstructured text
  • Try to assign logical and short names to namespaces used for your vocabularies, such as foaf:XXX, umbel:XXX or skos:XXX, with a maximimum of five letters preferred
  • Enable multi-lingual capabilities in all definitions and labels. This is a rather complicated best practice in its own right. For the time being, it means being attentive to the xml:lang=”en” (for English, in this case) property for all annotation properties
  • (If you disagree with these naming conventions, use your own, but in any event, be consistent!!).

Documentation Best Practices

  • Like good software programs, a properly constructed and commented ontology is the first requirement of best practice documentation
  • The entire ontology vocabulary should be documented via a dedicated system that allows finding, selecting and editing of any and all ontology terms and their properties
  • The methodologies should be documented for ontology construction and maintenance, including naming, selection, completeness and other criteria. Documents such as this one and others in this series provide examples of important supplementary documentation regarding methodology and practice
  • Provide a complete TechWiki-like documentation system for use cases, best practices, evaluation and testing metrics, tools installation and use, and all aspects of the ontology lifecycle should be provided and supported [22]
  • Develop a complete graph of the ontology and make it available via graph visualization tools to aid understanding of the ontology in its complete aspect [23], and
  • Other ample diagrams and flowcharts should also be prepared and made available for knowledge workers’ use. UML diagrams, for example, might be included here, but general workflows and concept relationships should be explicated in any case through visual means. Such diagrams are much easier to understand and follow than the actual ontology specification.

Organizational and Collaborative Best Practices

  • Collaboration is an implementation best practice [24]
  • Re-use of already agreed-up structures and vocabularies respects prior investments and needs to be emphasized
  • Improved processes for consensus making, including tools support, must be found to enable work groups to identify and decide upon terminology, definitions, alternative labels (SemSets), and relations between concepts. These processes need not be at the formal ontology level, but at the level of the concept graph underlying the ontology [24].

Testing Best Practices

  • Test new concepts, aided by proper domain, range and property restrictions; by invoking reasoners such that inconsistencies can be determined [25]
  • Test new properties, aided by invoking reasoners, which will identify inconsistencies [25]
  • Test via external class assignments, by linking to classes in external ontologies, which acts to ‘explode the domain’ [26]
  • Use external knowledge bases and ontologies, such as Cyc or UMBEL [27], to conduct coherency testing for the basic structure and relationships in the ontology
  • Evolve the ontology specification to include necessary and sufficient conditions [25] aid more complete reasoner testing for consistency and coherence.

Best Practices for Adaptive Ontologies

In the case of ontology-driven applications using adaptive ontologies [28], there are also additional instructions contained in the system (via administrative ontologies) that tell the system which types of widgets need to be invoked for different data types and attributes. This is different from the standard conceptual schema, but is nonetheless essential to how such applications are designed.

  • Use the structWSF middleware layer [29] as the abstract access point to:
    • To create, update, delete or otherwise manage data records
    • To browse or view existing records or record sets, based on simple to possible complex selection or filtering criteria, or
    • To take one of these results sets and progress it through various workflows involving specialized analysis, applications, or visualization.
  • Supplement the domain ontology with a semantic component ontology for the purposes of guiding data widget display and visualization [30], and
  • Supplement the domain ontology with the irON (instance record Object Notation) for dataset exchange and interoperability [31].

The administrative ontologies supporting these applications are managed differently than the standard domain ontologies that are the focus of most of the best practices above. Nonetheless, some of the domain ontology best practices work in tandem with them, the combination of which are called adaptive ontologies.

[1] This posting is part of a current series on ontology development and tools, now permanently archived and updated on the OpenStructs TechWiki. The series began with An Executive Intro to Ontologies, then continued with an update of the prior Ontology Tools listing, which now contains 185 tools. It progressed to a survey of ontology development methodologies. That led to a presentation of a new, Lightweight, Domain Ontologies Development Methodology. That piece was then expanded to address A New Landscape in Ontology Development Tools. This portion completes the series.
[2] Natalya F. Noy and Deborah L. McGuinness, 2001. “Ontology Development 101: A Guide to Creating Your First Ontology,” Stanford University Knowledge Systems Laboratory Technical Report KSL-01-05, March 2001. See
[3] Matthew Horridge et al., 2009. A Practical Guide to Building OWL Ontologies Using Protégé 4 and CO-ODE Tools, manual prepared by the University of Manchester, March 13, 2009. 108 pp. See
[4] Specialty search engines for ontologies include Swoogle, FalconS, Watson, Sindice and SWSE. In addition, one can use a general search engine such as Google with a search query such as <topic> owl:equivalentClass filetype:owl. Note the filetype might also include RDF or a variant such as N3, and other language-specific constructs of interest can also be substituted for the owl:equivalentClass.
[5] The TONES Ontology Repository is primarily designed to be a central location for ontologies that might be of use to tools developers for testing purposes. It has a nice browse facility, as well as filtering by OWL vocabulary. The system contains about 220 ontologies and is powered by the OWL API.
[6] is a semantic Web portal dedicated to ontology design patterns (ODPs). The portal was started under the NeOn project, which still partly supports its development.
[7] Elena Paslaru Bontas Simperl and Christoph Tempich, 2006. “Ontology Engineering: A Reality Check,” in Proceedings of the 5th International Conference on Ontologies, Databases, and Applications of Semantics ODBASE2006, 2006. See .
[9] Barry Smith et al., 2007. “The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration,” in Nature Biotechnology 25: 1251 – 1255, published online 7 November 2007; see
[10] See the NeOn networked ontologies project; see The four-year project began in 2006 and its first open source toolkit was released by the end of 2007. OWL features were added in 2008-09. NeON has since completed, though its toolkit and plug-ins can still be downloaded as open source.
[11] Brian Sletten, 2008. “Applying SKOS Concept Schemes,” on the DevX Web site, July 22, 2008; see
[12] M. K. Bergman, 2009. “Confronting Misconceptions with Adaptive Ontologies,” AI3:::Adaptive Information blog, Aug. 17, 2009.
[13] 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.
[14] Core ontologies are Dublin Core, DC Terms, Event, FOAF, GeoNames, SKOS, Timeline, and UMBEL. The various criteria that are considered in nominating an existing ontology to “core” status is that it should be general; highly used; universal; broad committee or community support; well done and documented; and easily understood. Though less universal, there are also a number of secondary ontologies, namely BIBO, DOAP, and SIOC.
[15] See Fausto Giunchiglia, Maurizio Marchese and Ilya Zaihrayeu, 2006. “Encoding Classifications into Lightweight Ontologies,” see Also, M. K. Bergman, 2010. “A New Methodology for Buidling Lightweight, Domain Ontologies,” AI3:::Adaptive Information blog, Sept. 1, 2010.
[16] Alistair Miles and Sean Bechhofer, eds., 2009. SKOS Simple Knowledge Organization System Reference, W3C Recommendation, 18 August 2009. See Some of the common SKOS predicates used in our ontologies include skos:definition, skos:prefLabel, skos:altLabel, skos:broaderTransitive, skos:narrowerTransitive.
[17] The TBox portion, or classes (concepts), is the basis of the ontologies. The ontologies establish the structure used for governing the conceptual relationships for that domain and in reference to external (Web) ontologies. The ABox portion, or instances (named entities), represents the specific, individual things that are the members of those classes. Named entities are the notable objects, persons, places, events, organizations and things of the world. Each named entity is related to one or more classes (concepts) to which it is a member. Named entities do not set the structure of the domain, but populate that structure. The ABox and TBox play different roles in the use and organization of the information and structure. These distinctions have their grounding in description logics.
[18] In the domain ontologies that are the focus here, we often want to treat our concepts as both classes and instances of a class.  This is known as “metamodeling” or “metaclassing” and is enabled by “punning” in OWL 2.  For example, here a case cited on the OWL 2 wiki entry on “punning“:
People sometimes want to have metaclasses. Imagine you want to model information about the animal kingdom. Hence, you introduce a class a:Eagle, and then you introduce instances of a:Eagle such as a:Harry.

(1) a:Eagle rdf:type owl:Class
(2) a:Harry rdf:type a:Eagle

Assume now that you want to say that “eagles are an endangered species”. You could do this by treating a:Eagle as an instance of a metaconcept a:Species, and then stating additionally that a:Eagle is an instance of a:EndangeredSpecies. Hence, you would like to say this:

(3) a:Eagle rdf:type a:Species
(4) a:Eagle rdf:type a:EndangeredSpecies.

This example comes from Boris Motik, 2005. “On the Properties of Metamodeling in OWL,” paper presented at ISWC 2005, Galway, Ireland, 2005.

“Punning” was introduced in OWL 2 and enables the same IRI to be used as a name for both a class and an individual. However, the direct model-theoretic semantics of OWL 2 DL accommodates this by understanding the class Father and the individual Father as two different views on the same IRI, i.e., they are interpreted semantically as if they were distinct. The technique listed in the main body triggers this treatment in an OWL 2-compliant editor. See further Pascal Hitzler et al., eds., 2009. OWL 2 Web Ontology Language Primer, a W3C Recommendation, 27 October 2009; see

[19] There is a role and place for closed world assumption (CWA) ontologies, though Structured Dynamics does not engage in them.

CWA is the traditional perspective of relational database systems within enterprises. The premise of CWA is that which is not known to be true is presumed to be false; or, any statement not known to be true is false. Another way of saying this is that everything is prohibited until it is permitted. CWA works well in bounded systems such as known product listings or known customer rosters, and is one reason why it is favored for transaction-oriented systems where completeness and performance are essential. In an ontology sense, CWA works best for bounded engineering environments such as aeronautics or petroleum engineering. Closed world ontologies also tend to be much more complicated with many nuanced predicates, and can be quite expensive to build.

The open world assumption (OWA), on the other hand, is premised that the lack of a given assertion or fact being available does not imply whether that possible assertion is true or false: it simply is not known. In other words, lack of knowledge does not imply falsity, and everything is permitted until it is prohibited. As a result, open world works better in knowledge environments with the incorporation of external information such as business intelligence, data warehousing, data integration and federation, and knowledge management.

See further, M. K. Bergman, 2009. “The Open World Assumption: Elephant in the Room,” AI3:::Adaptive Information blog, Dec. 21, 2009.

[20] See [3] for a good description of property restrictions in Section 4 and Appendix A.
[21] As another commentary on the importance of definitions, see
[22] The technical wiki (TechWiki) is the central repository for all documentation related to OpenStructs projects. TechWiki is the location for users and interested parties to learn about these projects and their applications, and for developers to author and write about their use and best practices. Both the TechWiki’s content and its software and organizatonal structure may be downloaded for free for setting up similar local technical documentation.
[23] See M. K. Bergman, 2008. “Large-scale RDF Graph Visualization Tools,” AI3:::Adaptive Information blog, Jan. 28, 2008; and M. K. Bergman, 2008. “Cytoscape: Hands-down Winner for Large-scale Graph Visualization,” AI3:::Adaptive Information blog, Jan. 28, 2008.
[24] The central role of ontologies is to describe a “worldview” and in specific organizations this means a shared understanding of the concepts, relations and terminology to describe the participants’ common domain. In turn, these shared understandings establish the semantics for how to effect communication and understanding within the population of domain users. All of this means that finding ways to identify and agree upon shared vocabularies and understandings is central to the task of modeling (creating an ontology) for the domain.

Sometimes this perception of shared views is too strictly interpreted as needing to have one and only one understanding of concepts and language. Far from it. One of the strengths of ontologies and language modeling within them is that multiple terms for the same concept or slight differences in understandings about nearly similar concepts can be accommodated. It is perfectly OK to have differences in terminology and concept understandings so long as those differences are also captured and explicated within the ontology. The recommendations herein that all concepts and terminology be defined, that SemSets be used to capture alternative ways to name concepts, and that concepts often be treated as both classes and instances are some of the best practices that reflect this approach.

So, while consensus building and collaboration methods are at the heart of effective ontology building, those methods need not also strive for a imposition of language and concepts by fiat. In fact, trying to do so undercuts the ability of the collaborative process to lead to greater shared understandings.

[25] See [3] for a good description of various testing and consistency checks in Sections 4.9 to 4.14.
[26] See Frédérick Giasson, 2008. “Exploding the Domain,” from his blog, April 20, 2008. ‘Exploding the domain’ means what happens when internal ontology concepts are linked to related ones on the external Web, which helps to bring in more information and context about the concept. It is also a way to test the coherence of the original concept.
[27] Already vetted knowledge bases can be a good reference testbed for testing the coherence of concepts in a new domain ontology. If the domain ontology describes concepts quite differently than standard practice (Wikipedia, Cyc and UMBEL are good for testing this), or if relationships between concepts are greatly at variance (Cyc and UMBEL are good for this), then there are likely coherency problems. In other domains other reference knowledge bases, more specific to the domain, can be used in similar ways.
[28] Structured Dynamics’ ontology-driven apps are generic applications, the operations of which are guided by the instructions and nature of the underlying data that feeds them. For example, in the case of a standard structured data display (say, a simple table like a Wikipedia infobox), such generic design includes templates tailored to various instance types (say, locational information presenting on a map versus people information warranting a image and vital statistics). Alternatively, in the generic design for a data visualization application using Adobe Flash, the information output of the results set contains certain formats and attributes, keyed by an administrative ontology linked by data type to a domain ontology’s results sets.

These ontology-driven apps, then, are informed structured results sets that are output in a form suitable to various intended applications. This output form can include a variety of serializations, formats or metadata. This flexibility of output is tailored to and responsive to particular generic applications; it is what makes our ontologies “adaptive”. Using this structure, it is possible to either “drive” queries and results sets selections via direct HTTP request or via simple dropdown selections on HTML forms. Similarly, it is possible with a single parameter change to drive either a visualization app or a structured table template from the equivalent query request. Ontology-driven apps through this ontology and architecture design thus provide two profound benefits. First, the entire system can be driven via simple selections or interactions without the need for any programming or technical expertise. And, second, simple additions of new and minor output converters can work to power entirely new applications available to the system.

[29] The structWSF Web services framework is generally RESTful middleware that provides a bridge between existing content and structure and content management systems and available indexing engines and RDF data stores. structWSF is a platform-independent means for distributed collaboration via an innovative dataset access paradigm. It has about twenty embedded Web services. See
[30] A semantic component is a Flex component that takes record descriptions and schema as input, and then outputs some (possibly interactive) visualizations of that record. Depending on the logic described in the input schema and the input record descriptions, the semantic component will behave differently to optimize its presentation to the users. About a dozen semantic components are available from the Semantic Component (Flex) Library. The Semantic Component Ontology is the governing structure for these schema.
[31] irON (instance record and Object Notation) is a abstract notation and associated vocabulary for specifying RDF triples and schema in non-RDF forms. Its purpose is to allow users and tools in non-RDF formats to stage interoperable datasets using RDF. The notation supports writing RDF and schema in JSON (irJSON), XML (irXML) and comma-delimited (CSV) formats (commON). The notation specification includes guidance for creating instance records (including in bulk), linkages to existing ontologies and schema, and schema definitions. Profiles and examples and code parsers and converters are also provided for the irXML, irJSON and commON serializations.
Posted:November 2, 2009

Structured Dynamics LLC

A New Slide Show Consolidates, Explains Recent Developments

Much has been happening on the Structured Dynamics front of late. Besides welcoming Steve Ardire as a senior advisor to the company, we also have been issuing a steady stream of new products from our semantic Web pipeline.

This new slide show attempts to capture these products and relate them to the various layers in Structured Dynamics’ enterprise product stack:

The show indicates the role of scones, irON, structWSF, UMBEL, conStruct and others and how they leverage existing information assets to enable the semantic enterprise. And, oh, by the way, all of this is done via Web-accessible linked data and our practical technologies.


Posted:September 18, 2009


Fred Giasson has just announced that Structured Dynamics has moved and is now hosting the new UMBEL Web services. Check out his “New Home for UMBEL Web Services” post to learn more.

I should mention that Structured Dynamics has also used this migration to update parts of its Web site, as well.

Posted by AI3's author, Mike Bergman Posted on September 18, 2009 at 5:05 pm in Structured Dynamics, UMBEL | Comments (0)
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Posted:September 2, 2009

Segmented UMBEL (Upper Mapping and Binding Exchange Layer)The Significant Advantages to a Logically Segmented TBox

The Message Understanding Conferences (MUC) were initiated in 1987 and financed by DARPA to encourage the development of new and better methods of information extraction (IE). It was a seminal series that resulted in basic measures of retrieval and semantic efficacy, recall (R) and precision (P) and the combined F-measure, and other core terminology and constructs used by IE today.

By the sixth version in the series (MUC-6), in 1995, the task of recognition of named entities and coreference was added. That initial slate of named entities included the basic building blocks of person (PER), location (LOC), and organization (ORG); to these were added the numeric building blocks of time, percentage or quantity. The very terminology of named entity was coined for this seminal meeting, as was the idea of inline markup [1].

What is a ‘Nameable Thing’?

The intuition surrounding “named entity” and nameable “things” was that they were discrete and disjoint. A rock is not a person and is not a chemical or an event. As initially used, all “named entities” were distinct individuals. But, there also emerged the understanding that some classes of things could also be treated as more-or-less distinct nameable “things”: beetles are not the same as frogs and are not the same as rocks. While some of these “things” might be a true individual with a discrete name, such as Kermit the Frog, or The Rock at Northwestern University, most instances of such things are unnamed.

The “nameability” (or logical categorization) of things is perhaps best kept separate from other epistemological issues of distinguishing sets, collections, or classes from individuals, members or instances.

In a closed-world system it is easier to enforce clean distinctions. The Cyc knowledge base, for example, the basis for UMBEL (Upper Mapping and Binding Exchange Layer),  makes clear the distinction between individuals and collections. In the semantic Web and RDF, this can become smeared a bit with the favored terminology shifting to instances and classes, and in pragmatic, real-world terms we (as humans) readily distinguish John Smith as distinct from Jane Doe but don’t generally (unless we’re entomologists!) make such distinctions for individual beetles, let alone entire genera or species of beetles.

Under precise conditions, these distinctions are important. The fact that Cyc, for example, is assiduous in its application of these distinctions is a major reason for the overall coherence of its knowledge base. But, for most circumstances, we think it is OK to accept a distinction between “nameable” things such as frogs and beetles, but also to accept that there may be nameable individuals at times in those groupings such as Kermit that are truly an individual in that more refined sense.

This digression sets the background for a natural progression from that first MUC-6 conference. If we could cluster persons or organizations, why not other categories of distinct and disjoint things such as frogs or beetles or rocks?

From the first six entity categories of MUC-6 we begin to see an expansion to broader coverage. Readers of this blog will recall that I have been a fan for quite some time of the expanded coverage of 64 classes of entities proposed by BBN or the 200 proposed by Sekine [2] (as discussed, for example in the April 2008 Subject Concepts and Named Entities article). Again, the intuition was that real things in the real world could be logically categorized into discrete and disjoint categories.

Thus, “named entities” inexorably moved to become a categorization system, where the degree of familiarity and distinction dictated whether it was the individual (with a unique name, such as Abraham Lincoln or Mt. Rushmore) or groupings such as animal or plant species and their common names (such as beetle or oak) that was the standard “handle” for assigning a name to the “nameable thing”.

While many can argue these individual <–> grouping distinctions and whether we are talking about true, unique, named individuals or names of convenience, I think that (at least for this blog post and discussion), that misses the real, fundamental point.

The real, fundamental point is that some “things” (whether individuals, instances or classes) are distinct from other “things”. Such disjoint distinctions are a powerful concept that should not be lost sight of by “angels dancing on the head of a pin” epistemological arguments. A frog is not a rock, despite neither are “individuals”, and how can we take advantage of that realilty?

What Works for Entities, Works for Concepts

Nearly from the outset of our work with UMBEL as a ‘TBox’ [3] — that is, as a set of 20,000 or so common “subject concepts” — the natural question was what the relation or correspondence was of these concepts to the underlying “things” (entities) that they organized. As we probed the disjoint categories within the Sekine 200 entity types, for example, we began to see significant parallels and overlap. Also gnawing at our sense of order was the rather artificial and arbitrary class of concepts in UMBEL that we termed “Abstract Concepts”.

We introduced Abstract Concepts in the first release of UMBEL. When introduced, we defined “Abstract concepts [as] representing abstract or ephemeral notions such as truth, beauty, evil or justice, or [as] thought constructs useful to organizing or categorizing things but are not readily seen in the experiential world.” In pragmatic terms, Abstract Concepts in UMBEL were often pivotal nodes in the UMBEL subject graph necessary to maintain a high degree of concept interconnectivity.

In any world view that attempts to be more-or-less comprehensive, there is a gradation of concepts from the concrete and observable to the abstract and ephemeral. The recognition that some of these concepts may be more abstract, then, was not the issue. The issue was that there was no definable basis for segregating a concrete Subject Concept from the more Abstract Concept. Where was the bright line? What was the actionable distinction?

Off and on we have probed this question for more than a year, and have looked at what might constitute a more natural and logical ordering and segmentation within UMBEL. After many tests and detailed analysis, we are now releasing the first results of our investigations.

For, like nameable entities or things, we can see a logical segmentation of (mostly) disjoint concepts within the UMBEL TBox. Here are the summary percentages of these high-level splits:

Disjoint Concepts 90%
Attributes 1%
Classifications 9%
TOTAL 100%

(Because the analysis is still being refined, exact counts and percentages for the 20,000 concepts in UMBEL are not provided.)

Why a Logical Segmentation?

As we dove deeper into these ideas, not only could we see the basis for a logical segmentation within UMBEL’s concepts, but manifest benefits from doing so as well. Remember that UMBEL’s concept structure performs two main roles. It:  1) provides a coherent framework for relating and “mapping” other external ontologies; and 2) provides conceptual binding points for organizing entities and instances [4]. Via logical segmentation, we get benefits for both roles.

Here are some of the broad areas of benefit from a logical UMBEL segmentation that we have identified:

  • Template-driven — as we discuss elsewhere, Structured Dynamics also uses its ontologies to “drive applications” and the user interfaces (UI) that support them. By proper segmentation of UMBEL concepts, we are able to determine to what “cluster” of things (which we call either dimensions or superTypes; see below) a given thing belongs. This identification means we can also determine how best to display information about that “thing”. This determination can include either the attributes or the display templates appropriate for that thing. For example, location-based things or time-based things might invoke map or calendar or timeline type displays. Moreover, because of the logical segmentation of concepts, we can also use the power of the concept graph to infer more generic display templates when specific matches are absent
  • Computational Efficiency — as the percentages above indicate, once we identify what superType concept to which a given instance belongs, we can eliminate nearly all remaining UMBEL concepts from consideration. This logical winnowing leads to computational efficiencies at all levels in the system. The fastest computational work is not to do it, and when large chunks of data are removed from consideration, many performance advantages accrue
  • Disambiguation — via this approach we now can assess concept matches in addition to entity matches. This means we can triangulate between the two assessments to aid disambiguation. Because of these logical segmentations, we also have multiple “clusters” (that is, either the concept, type, superType or dimension) upon which to do our disambiguation evaluations, either between concepts and entities or within the various concept clusters. We can do so via either multiple semantic vectors (for statistical-based methods) or multiple features (for machine learning methods). In other words, because of logical segmentation, we have increased the informational power of our concept graph
  • Structure and Integrity Testing — the very mindset of looking for logical segmentation has led to much learning about the UMBEL structure and OpenCyc upon which it is based. In the process, missing nodes (concepts), erroneous assignments, and superfluous nodes are all being discovered. Further, many of these tests can be automated using basic logical and inference approaches. The net result is a constant improvement to the scope and completeness of the structure. Lastly, these same approaches can be applied when mapping external ontologies to UMBEL, providing similar consistency benefits.

With these benefits in mind, we have undertaken concerted analysis of UMBEL to discern what this “logical segmentation” might be. This investigation has occurred over three concentrated periods over the past year. (Intervening priorities or other work prevented concentrating solely on this task.)

We are now complete with our first full iteraton of investigation. In this post, and then the subsequent release of UMBEL version 0.80 in the coming weeks, the fruits of this effort should be evident. However, it should also be noted that we are still learning much from this new mindset and approach. UMBEL structure refinement may be likely for some time to come.

UMBEL Analysis

Most things and concepts about them are based on real, observable, physical things in the real world. Because most of these things can not occupy both the same moment in time and the same location in physical space, a useful criterion for looking at these things and concepts is disjointedness.

In a broad sense, then, we can split our concepts of the world between those ideas that are disjoint because they pertain to separable objects or ideas and those that are cross-cutting or organizational or classificatory. Attributes, such as color (pink, for example), are often cross-cutting in that they can be used to describe quite disparate things. Inherent classification schemes such as academic fields of study or library catalog systems — while useful ways to organize the world — are not themselves in-and-of the world or discrete from other ideas. Thus, classificatory or organizational concepts are inherently not disjoint.

With the criterion of disjointedness in hand, then, we began an evaluation process of the UMBEL subject concepts. We looked to organizational schema such as the entity types of Sekine or BBN for some starting guidance. We also kept in mind that we also wanted our categories to inform logical clusterings of possible data presentation, such as media types or locations or time.

For terminology, we adopted the term superType to denote the largest cluster designation upon which this disjointedness may occur. As a way to test the basic coherence of these superTypes, we also collected them into larger groups which we termed dimensions.

Our analysis process began with branch-by-branch testing of the UMBEL concept graph using automated scripts, attempting to find pivotal nodes where child instance members were disjoint from other superTypes. This we term the “top-down” method.

This automated analysis was then supplemented with a complete manual inspection of all unassigned and assigned concepts, with a “bottom up” assignment of concepts or corrections to the automated approach. This inspection then led to new insights and identification of missing concepts that needed to be added into UMBEL.

We are still converging between these two methods. Optimally, we should be able to tease out all UMBEL superTypes with a relatively few number of union, intersection, or complement set operations. In its current form, we are close, but there are still some rough spots.

Nonetheless, this analysis method has led us to identify some 33 superTypes [5], clustered into 9 dimensions. Of these, 29 superTypes and 8 dimensions are mostly disjoint. The one dimension of Classificatory includes the four cross-cutting superTypes of attributes and organizational schema that can apply to any of the 29 disjoint superTypes.

UMBEL superTypes

Here is the schema, with the descriptions of each:

Dimension superType Description/Sub-types
Natural World Natural Phenomena This superType includes natural phenomena and natural processes such as weather, weathering, erosion, fires, lightning, earthquakes, tectonics, etc. Clouds and weather processes are specifically included. Also includes climate cycles, general natural events (such as hurricanes) that are not specifically named, and biochemical processes and pathways.
Natural Substances Notable inclusions are minerals, compounds, chemicals, or physical objects that are not the outcome of purposeful human effort, but are found naturally occurring. Other natural objects (such as rock, fossil, etc.) are also found under this superType.
Earthscape The Earthscape superType consists mostly of the collection of cartographic features that occur on the surface of the Earth. Positive examples include Mountain, Ocean, and Mesa. Artificial features such as canals are excluded. Most instances of these features have a fixed location in space.Underground and underwater are also explicitly contained.This superType is explicitly disjoint with Extraterrestrial (see below).
Extraterrestrial This superType includes all natural things not specifically terrestrial, including celestial bodies (planets, asteroids, stars, galaxies, etc., that can be located within a sky map)
Living Things Prokaryotes The Prokaryotes include all prokaryotic organisms, including the Monera, Archaebacteria, Bacteria, and Blue-green algas. Also included in this superType are viruses and prions.
Protists or Fungus This is the remaining cluster of eukaryotic organisms, specifically including the fungus and the protista (protozoans and slime molds).
Plants This superType includes all plant types and flora, including flowering plants, algae, non-flowering plants, gymnosperms, cycads, and plant parts and body types. Note that all Plant Parts are also included.
Animals This large superType includes all animal types, including specific animal types and vertebrates, invertebrates, insects, crustaceans, fish, reptiles, amphibia, birds, mammals, and animal body parts. Animal parts are specifically included. Also, groupings of such animals are included. Humans, as an animal, are included (versus as an individual Person). Diseases are specifically excluded.
Diseases Diseases are atypical or unusual or unhealthy conditions for (mostly human) living things, generally known as conditions, disorders, infections, diseases or syndromes. Diseases only affect living things and sometimes are caused by living things. This superType also includes impairments, disease vectors, wounds and injuries, and poisoning
Person Types The appropriate superType for all named, individual human beings. This superType also includes the assignment of formal, honorific or cultural titles given to specific human individuals. It further includes names given to humans who conduct specific jobs or activities (the latter case is known as an avocation). Examples include steelworker, waitress, lawyer, plumber, artisan. Ethnic groups are specifically included.
Human Activities Organizations Organization is a broad superType and includes formal collections of humans, sometimes by legal means, charter, agreement or some mode of formal understanding. Examples include geopolitical entities such as nations, municipalities or countries; or companies, institutes, governments, universities, militaries, political parties, game groups, international organizations, trade associations, etc. All institutions, for example, are organizations.Also included are informal collections of humans. Informal or less defined groupings of humans may result from ethnicity or tribes or nationality or from shared interests (such as social networks or mailing lists) or expertise (“communities of practice”). This dimension also includes the notion of identifiable human groups with set members at any given point in time. Examples include music groups, cast members of a play, directors on a corporate Board, TV show members, gangs, mobs, juries, generations, minorities, etc.Finally, Organizations contain the concepts of Industries and Programs and Communities.
Finance & Economy This superType pertains to all things financial and with respect to the economy, including chartable company performance, stock index entities, money, local currencies, taxes, incomes, accounts and accounting, mortgages and property.
Culture, Issues, Beliefs This category includes concepts related to political systems, laws, rules or cultural mores governing societal or community behavior, or doctrinal, faith or religious bases or entities (such as gods, angels, totems) governing spiritual human matters. Culture, Issues, beliefs and various activisms (most -isms) are included
Activities These are ongoing activities that result (mostly) from human effort, often conducted by organizations to assist other organizations or individuals (in which case they are known as services, such as medicine, law, printing, consulting or teaching) or individual or group efforts for leisure, fun, sports, games or personal interests (activities)
Human Works Products This is the largest superType and includes any instance offered for sale or performed as a commercial service. Often physical object made by humans that is not a conceptual work or a facility, such as vehicles, cars, trains, aircraft, spaceships, ships, foods, beverages, clothes, drugs, weapons. Products also include the concept of ‘state’ (e/g/., on/off)
Food or Drink This superType is any edible substance grown, made or harvested by humans. The category also specifically includes the concept of cuisines
Drugs This superType is an drug, medication or addictive substance
Facilities Facilities are physical places or buildings constructed by humans, such as schools, public institutions, markets, museums, amusement parks, worship places, stations, airports, ports, carstops, lines, railroads, roads, waterways, tunnels, bridges, parks, sport facilities, monuments. All can be geospatially located.Facilities also include animal pens and enclosures and general human “activity” areas (golf course, archeology sites, etc.). Importantly, Facilities include infrastructure systems such as roadways and physical networks.Facilities also include the component parts that go into making them (such as foundations, doors, windows, roofs, etc.)
Information Chemistry (n.o.c) This superType is a residual category (n.o.c., not otherwise categorized) for chemical bonds, chemical composition groupings, and the like. It is formed by what is not a natural substance or living thing (organic) substance.
Audio Info This superType is for any audio-only human work. Examples include live music performances, record albums, or radio shows or individual radio broadcasts
Visual Info This superType includes any still image or picture or streaming video human work, with or without audio. Examples include graphics, pictures, movies, TV shows, individual shows from a TV show, etc.
Written Info This superType includes any general material written by humans including books, blogs, articles, manuscripts, but any written information conveyed via text.
Structured Info This information superType is for all kinds of structured information and datasets, including computer programs, databases, files, Web pages and structured data that can be presented in tabular form
Notations & References Akin to conceptual works, these are codified means of human expression. Examples range from human languages themselves, to more domain-specific cases such as chemical symbols, genetic code (A-G-C-T), protocols, and computer languages, mathematical and set notations, etc.Identifiers (numeric or alphanumeric identifiers for objects, often in a highly patterned way, such as phone numbers, URLs, zip and postal codes, SKUs, product codes, etc.), Units (any of the various ways in which measurement, space, volume, weight, speed, intensity, temperature, calories, siesmic intensity or other quantitative descriptions of phenomena can be made) and key reference types are also included in this superType
Numbers This unique superType is for any abstract representation of numbers and numerics
Human Places Geopolitical Named places that have some informal or formal political (authorized) component. Important subcollections include Country, IndependentCountry, State_Geopolitical, City, and Province.
Workplaces, etc. These are various workplaces and areas of human activities, ranging from single person workstations to large aggregations of people (but which are not formal political entities)
Time-related Events These are nameable occasions, games, sports events, conferences, natural phenomena, natural disasters, wars, incidents, anniversaries, holidays, or notable moments or periods in time
Time This superType is for specific time or date or period (such as eras, or days, weeks, months type intervals) references in various formats
Descriptive Attributes This general superType category is for descriptive attributes of all kinds. Think of the specific attributes in Wikipedia “infoboxes” to understand the purpose and coverage of this superType. It includes colors, shapes, sizes, or other descriptive characteristics about an object
Classificatory Abstract-level This general superType category is largely composed of former AbstractConcepts, and represent some of the more abstract upper-level nodes for connecting the UMBEL structure together. This superType also includes theories or processes or methods for humans to do stuff or any human technology
Topics/Categories This largely subject-oriented superType is a means for using controlled vocabularies and classification schemes for characterizing what content “is about”. The key constituents of this category are Types, Classifications, Concepts, Topics, and controlled vocabularies
Markets & Industries This superType is a specialized classificatory system for markets and industries. It could be combined with the superType above, but is kept separate in order to provide a separate, economy-oriented system.

These may undergo some further refinement prior to release of UMBEL v 0.80, and some of the definitions will be tightened up.

(Note: It should also be mentioned that some of these superTypes further lend themselves to further splits and analysis. The Product superType, for example, is ripe for such treatment.)

Distribution of superTypes

The following diagram shows the distribution of these 20,000 UMBEL concepts across major area. By far the largest superType is Products, even with further splits into Food and Drinks and Pharmaceuticals. The next largest categories are Person and Places and Events superTypes, with Organizations and Animals not far behind:

Even in its generic state, UMBEL provides a very rich vocabulary for describing things or for tying in more detailed external ontologies. There are nearly 5,000 concepts across products of all types, for example.

Possible Overlaps (non-disjoint) between superTypes

You may recall that our analysis showed 29 of the superTypes to be “mostly disjoint.”  This is because there are some concepts — say, MusicPerformingAgent — that can apply to either a person or a group (band or orchestra, for example). Thus, for this concept alone, we have a bit of overlap between the normally disjoint Person and Organization superTypes.

The following shows the resulting interaction matrix where there may be some overlap between superTypes:

This kind of interaction diagram is also useful for further analyzing the concept graph structure, as well.

Even Where Overlaps Occur, They are Minor

Of the 29 “mostly” disjoint superTypes, only a relatively few show potential interactions, and then only in minor ways. We can illustrate this (drawn to scale) for the interaction between the Product, Food & Drink and Drug (Pharmaceuticals) superTypes, with the fully disjoint Organization superType thrown in for comparison:

Example superTypes Overlap

Across all 20,000 concepts, then, fully 85% are disjoint from one another (5% is lost due to overlaps between “mostly” disjoint superTypes). This is a surprising high percentage, with even better likelihood to deliver the benefits previously noted.

Interim Conclusions and Observations

These are exciting findings that bode well for UMBEL’s ongoing role and usefulness. Also, the very detailed analysis that has led to these interim findings very much reaffirms the wisdom of basing UMBEL on Cyc.  Cyc showed itself to be admirably coherent and remarkably complete. (It also appears that the first versions of UMBEL were also extracted well in terms of good coverage.)

This approach now gives us an understandable and defensible basis for logical segementation of UMBEL. It also provides a much-desired alternative to the earlier Abstract Concepts, which will now be dropped entirely as a schema concept.

One area deserving further attention is in the Attribute superType. We are in the process, for example, of analyzing attributes across Wikipedia and need to look through a slightly different lens at this superType [6]. This area is further important in its strong interaction with the Instance Record Vocabulary that is accompanying this effort on the entity side.

Another lesson for us has been to back away from the terminology of named entity, introduced at MUC-6. The expansions of that idea into other “nameable” things has caused us to embrace the “instance” nomenclature, as evidenced by our emerging IRV.

It is rewarding to prepare this next iteration release of UMBEL with its new mindset of logical segmentation and disjointedness. But — what is also clear — there are many treasures left to mine still hidden in the inherent structure of UMBEL and its Cyc parent.

[1] The original labels were ENAMEX for entity named expression and NUMEX for numeric expression. The markup format specified was also SGML. For an interesting history of this MUC-6 watershed, see Ralph Grishman and Beth Sundheim, 1996. Message Understanding Conference – 6: A Brief History, in Proceedings of the 16th International Conference on Computational Linguistics (COLING), I, Kopenhagen, 1996, 466–471.

[2] In a named entity, the word named applies to entities that have a “rigid designators” as defined by Kripke for the referent. For instance, the automotive company created by Henry Ford in 1903 is referred to as Ford or Ford Motor Company. Rigid designators include proper names as well as certain natural kind of terms like biological species and substances.Sekine’s extended hierarchy proposed in 2002 is made up of 200 subtypes, with 32 larger clusters within that. Here is the top level of the Sekine type system:

Name-Other Title Timex Frequency
Person Unit Periodx Rank
Organization Vocation Numex-Other Age
Location Disease Money School Age
Facility God Stock Index Latitude Longitude
Product ID Number Point Measurement
Event Color Percent Countx
Natural Object Time-Other Multiplication Ordinal Number

Though developed separately and for different purposes, BBN categories also proposed in 2002 consists of 29 types and 64 subtypes. Here are the BBN types (Note: BBN claims 29 types because there are double entries or considerations for the first five entries):

Person Time Animal
NORP (adjectival GPEs) Percent Substance
Facility Money Disease
Organization Quantity Work of Art
GPE (geopolitical places) Ordinal Law
Location Cardinal Language
Product Events Contact Info
Date Plant Game

Of course, other entity extraction systems have similar clusterings and approaches. Though less formal in the sense of a hierarchy or purported complete entity coverage, here for example is the listing of entity types within Calais:

Anniversary FaxNumber NaturalFeature RadioProgram
City Holiday OperatingSystem RadioStation
Company IndustryTerm Organization Region
Continent MarketIndex Person SportsEvent
Country MedicalCondition PhoneNumber SportsGame
Currency Movie Position SportsLeague
EmailAddress MusicAlbum Product Technology
EntertainmentAwardEvent MusicGroup ProgrammingLanguage TVShow
Facility NaturalDisaster ProvinceOrState TVStation
PublishedMedium URL

See further the Wikipedia entry on named entity recognition.

[3] We use the reference to “TBox” in accordance with our working definition for description logics:

“Description logics and their semantics traditionally split concepts and their relationships from the different treatment of instances and their attributes and roles, expressed as fact assertions. The concept split is known as the TBox (for terminological knowledge, the basis for T in TBox) and represents the schema or taxonomy of the domain at hand. The TBox is the structural and intensional component of conceptual relationships. The second split of instances is known as the ABox (for assertions, the basis for A in ABox) and describes the attributes of instances (and individuals), the roles between instances, and other assertions about instances regarding their class membership with the TBox concepts.”
[4] UMBEL also provides a SKOS-based vocabulary extension for describing other domains and mappings between classes and instances. This purpose, however, is outside of the scope of this current article.
[5] As a reference roadmap, UMBEL was specifically designed not to include meronymous (part of) relationships (see further this reference). Thus, all “part of” type concepts were assigned to the whole superType category for which they are a part. Thus, “animal parts” are assigned to the superType Animal; “car parts” to the superType Product.
[6] For a general discussion of attributes and their relation to entities, see Satoshi Sekine, 2008. Extended Named Entity Ontology with Attribute Information, in Proceedings of the 6th edition of the Language Resources and Evaluation Conference (LREC 2008). Marrakech, Morocco. See