Abstract
Ontologies are supposed to address the problem of making information systems’ conceptual models shareable and understandable. Most often, however, ontologies are nothing but structured lexical resources, which bring with them the classic problem behind natural language meanings: how to make sure that names and predicates are consistently interpreted all through the information sphere? Here is where formal ontology comes to play. In fact, the ‘ontological level’ [1, 3] is where, thanks to formal constraints (meaning axioms), unintended models (spurious interpretations) should be cut off. Yet, interpreting well-founded, highly formalized ontologies is far from trivial, and does not come for free. What makes ontology so difficult in practice? How to make concepts understandable and alleviate the burden of mapping strict ontological specifications with business data? This short paper will provide a brief overview on common issues when working with formal ontology and how to address them in practice, and will give some hint on effective usages of highly formalized shared conceptual models for business information systems.
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Aristotle. Metaphysics. I.8.
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Vetere, G. (2019). Formal Ontology in a Relativistic Setting. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_34
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