In the DOGMA ontology engineering approach ontology construction starts from a (possibly very large) uninterpreted base of elementary fact types called lexons that are mined from linguistic descriptions (be it from existing schemas, a text corpus or formulated by domain experts). An ontological commitment to such ”lexon base” means selecting/reusing from it a meaningful set of facts that approximates well the intended conceptualization, followed by the addition of a set of constraints, or rules, to this subset. The commitment process is inspired by the fact-based database modeling method NIAM/ORM2, which features a recently updated, extensive graphical support. However, for encouraging lexon reuse by ontology engineers a more scalable way of visually browsing a large Lexon Base is important. Existing techniques for similar semantic networks rather focus on graphical distance between concepts and not always consider the possibility that concepts might be (fact-) related to a large number of other concepts. In this paper we introduce an alternative approach to browsing large fact-based diagrams in general, which we apply to lexon base browsing and selecting for building ontological commitments in particular. We show that specific characteristics of DOGMA such as grouping by contexts and its ”double articulation principle”, viz. explicit separation between lexons and an application’s commitment to them can increase the scalability of this approach. We illustrate with a real-world case study.
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- T-Lex: A Role-Based Ontology Engineering Tool
Pieter De Leenheer
- Springer Berlin Heidelberg