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Frequently Asked Questions

  1. What is the difference between the TAX and the GEN classes?
  2. The TAX classes can reflect pretty bad modeling choices - are they of any use?
  3. The GEN classes are not constrained by axioms - are they of any use?


  1. What is the difference between the TAX and the GEN classes?
    The TAX classes (for "taxonomic") represent the meaning of the original label as a broad category - approximately "Anything that can in any relevant context be classified under the respective label". This is the formal meaning of a category label in the unprocessed categorization schema. The GEN classes (for "generic") represent ontology classes that narrow down this broad meaning to objects of a certain kinds. See the GenTax section of this Web page for more details.

  2. The TAX classes can reflect pretty bad modeling choices - are they of any use?
    Indeed, such classes often tangle objects of a very different ontological nature: objects, roles, or happenings. Clean, "handcrafted" ontology classes are of course better. However, for many domains we don’t have such clean ontologies available, and the TAX classes may be better than nothing, since they can be generated cheaply from existing Knowledge Organization Systems (KOS).
    Also, we may have large data assets that are classified using the original KOS. A cleaner ontology requires refining all existing assignments of objects to ontology classes, which may not be feasible.

  3. The GEN classes are not constrained by axioms - are they of any use?
    It is of course desirable to capture more of the intended semantics of the GEN classes axiomatically. Unfortunately, there is currently no generic, fully automated approach for that. Also, as we have shown in more detail in [1], axioms are necessary only for 3 of the six beneficial effects of ontologies.
    The ontologies created by SKOS2OWL can be enriched by axioms either manually using standard tooling or using various heuristics and semi-automated approaches, e.g. for learning disjointness, as shown in [2].

[1] Hepp, Martin: Ontologies: State of the Art, Business Potential, and Grand Challenges, in: Hepp, M.; De Leenheer, P.; de Moor, A.; Sure,Y. (Eds.): Ontology Management: Semantic Web, Semantic Web Services, and Business Applications, ISBN 978-0-387-69899-1, Springer, 2007, pp. 3-22. http://www.heppnetz.de/files/hepp-ontologies-state-of-the%20art.pdf
[2] Johanna Völker, Denny Vrandecic, York Sure, Andreas Hotho: Learning Disjointness, in Enrico Franconi, Michael Kifer, Wolfgang May, Proceedings of the 4th European Semantic Web Conference (ESWC'07), volume volume 4519 of Lecture Notes in Computer Science, pp. 175-189. Springer, June 2007. http://www.aifb.de/WBS/jvo/publications/disjointness_2007.pdf