Abstract
The design of ontologies is a time-consuming and resource-intensive endeavour. Rather than (manually) design the ontology first and then associate it with data, can we (semiautomatically) design the ontology from the data itself? This paper presents a novel approach to the semi-automated design of ontologies that incorporates axiom generation from data models, semantic parsing, and ontology learning from examples and counterexamples via search through an ontology repository.
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Notes
- 1.
A model of a logical theory is a truth assignment for the relations in the signature of the theory that satisfies all sentences in the theory.
- 2.
We follow previous work in terminology and notation [9] treating ontologies and their modules as logical theories. We do not distinguish between logically equivalent theories. For every theory T, \(\Sigma (T)\) denotes its signature, which includes all the constant, function, and relation symbols used in T, and \(\mathcal {L}(T)\) denotes the language of T, which is the set of first-order formulæthat only use the symbols in \(\Sigma (T)\).
- 3.
The Common Logic axioms for all theories in this Figure can be found at: https://github.com/gruninger/colore/tree/master/ontologies/bipartite_incidence.
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Grüninger, M., Chow, A., Wong, J. (2024). Semiautomatic Design of Ontologies. In: Almeida, J.P.A., Kaczmarek-Heß, M., Koschmider, A., Proper, H.A. (eds) The Practice of Enterprise Modeling. PoEM 2023. Lecture Notes in Business Information Processing, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-48583-1_9
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