Abstract
Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital — yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic \(\mathcal {EL}\) and define corresponding learning algorithms.
This work was funded in part by the National Research Foundation under Grant no. 85482.
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Notes
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An alternative, more general approach can be defined in terms of specific fragments of models. Such generalization, which lies beyond the scope of this paper, is essential when the learning problem concerns languages without finite model property.
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Klarman, S., Britz, K. (2016). Ontology Learning from Interpretations in Lightweight Description Logics. In: Inoue, K., Ohwada, H., Yamamoto, A. (eds) Inductive Logic Programming. ILP 2015. Lecture Notes in Computer Science(), vol 9575. Springer, Cham. https://doi.org/10.1007/978-3-319-40566-7_6
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