DOGMA-MESS: A Tool for Fact-Oriented Collaborative Ontology Evolution

Ontologies being shared formal specifications of a domain, are an important lever for developing meaningful internet systems. However, the problem is not in what ontologies are, but how they become operationally relevant and sustainable over longer periods of time. Fact-oriented and layered approaches such as DOGMA have been successful in facilitating domain experts in representing and understanding semantically stable ontologies, while emphasising reusability and scalability. DOGMA-MESS, extending DOGMA, is a collaborative ontology evolution methodology that supports stakeholders in iteratively interpreting and modeling their common ontologies in their own terminology and context, and feeding back these results to the owning community. In this paper we extend DOGMA Studio with a set of collaborative ontology evolution support modules.

De Leenheer, P. and Debruyne C. (2008) DOGMA-MESS: A Tool for Fact-Oriented Collaborative Ontology Evolution. In Proc. of On the Move to Meaningful Internet Systems 2008: ORM (ORM 2008) (Monterrey, Mexico), LNCS 5333, Springer, pp. 797-806

T-Lex: a Role-based Ontology Engineering Tool

Trog, D., Vereecken, J., Christiaens, S., De Leenheer, P., and Meersman, R. (2006) T-Lex: a Role-based Ontology Engineering Tool. In Proc. of the On The Move to Meaningful Internet Systems Workshops (OTM2006) (Montpelier, France) , LNCS 4278, Springer, pp. 1191-1200.

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.

In Collibra, the T-lex  plugin has been further extended in the Collibra Workbench to visualise business semantics engineering.

Disambiguation of Natural-language Terms in Business Semantics Engineering

De Leenheer, P. and de Moor, A. (2005) Context-driven Disambiguation in Ontology Elicitation. In Shvaiko P. & Euzenat J.,(eds.), Context and Ontologies: Theory, Practice and Applications, AAAI Technical Report WS-05-01 (Pittsburgh, Pennsylvania), AAAI Press, pp. 17–24.

Illustration of the two levels in DOGMA ontology: on the left -- the lexical level, lexons are elicited from various contexts. On the right, there is the conceptual level consisting of a concept definition server. The meaning ladder in between illustrates the articulation of lexical terms into concept definitions.

Ontologies represent rich semantics in a lexical way. Lexical labels are used to identify concepts and relationships, though there is no bijective mapping between them. Phenomenons such as synonyms and homonyms exemplify this, and can result in frustrating misunderstanding and ambiguity. In the elicitation and application of ontologies, the meaning of the ontological knowledge is dependent on the context. We consider the role of context in ontology elicitation by introducing context in a concept definition server for ontology representation. We also adopt other features of context found in literature, such as packaging of knowledge, aligning elements of different contexts, and reasoning about contexts. Finally, we illustrate context-driven ontology elicitation with a real world case study.

This paper was further extended in Journal of Data Semantics. See my related blog in this. In our business semantics management approach, disambiguation forms an important but difficult exercise during the consolidation activities.