Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Propositional, first-order, and higher-order logics (Appendix A)
Natural language processing and knowledge representation
Conceptual analysis of lexical taxonomies: the case of WordNet top-level
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
Data & Knowledge Engineering
The Knowledge Engineering Review
Mind the gap!: Transcending the tunnel view on ontology engineering
ICPW '07 Proceedings of the 2nd international conference on Pragmatic web
Let's talk about our “being”: A linguistic-based ontology framework for coordinating agents
Applied Ontology - Formal Ontologies for Communicating Agents
Ontology Engineering --- The DOGMA Approach
Advances in Web Semantics I
Towards automated reasoning on ORM schemes mapping ORM into the DLRidf description logic
ER'07 Proceedings of the 26th international conference on Conceptual modeling
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Unsatisfiability reasoning in ORM conceptual schemes
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
Let's talk about our “being”: A linguistic-based ontology framework for coordinating agents
Applied Ontology - Formal Ontologies for Communicating Agents
On constructing semantic decision tables
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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In this paper, we first introduce the notion of gloss for ontology engineering purposes. We propose that each vocabulary in an ontology should have a gloss. A gloss basically is an informal description of the meaning of a vocabulary that is supposed to render factual and critical knowledge to understanding a concept, but that is unreasonable or very difficult to formalize and/or articulate formally. We present a set of guidelines on what should and should not be provided in a gloss. Second, we propose to incorporate linguistic resources in the ontology engineering process. We clarify the importance of using lexical resources as a "consensus reference" in ontology engineering, and so enabling the adoption of the glosses found in these resources. A linguistic resource (i.e. its list of terms and their definitions) shall be seen as a shared vocabulary space for ontologies. We present an ontology engineering software tool (called DogmaModeler), and illustrate its support of reusing of WordNet's terms and glosses in ontology modeling.