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 war rant finite learnability of TBoxes expressed in selected fragments of the Description Logic EL and define corresponding learning algorithms.
Reference:
Klarman, S and Britz, K. 2015. Ontology learning from interpretations in lightweight description logics. In: The 25th International Conference on Inductive Logic programming (ILP), Kyoto, Japan, 20-22 August 2015
Klarman, S., & Britz, K. (2015). Ontology learning from interpretations in lightweight description logics. 25th International Conference On Inductive Logic Programming. http://hdl.handle.net/10204/8402
Klarman, S, and K Britz. "Ontology learning from interpretations in lightweight description logics." (2015): http://hdl.handle.net/10204/8402
Klarman S, Britz K, Ontology learning from interpretations in lightweight description logics; 25th International Conference On Inductive Logic Programming; 2015. http://hdl.handle.net/10204/8402 .
The 25th International Conference on Inductive Logic programming (ILP), Kyoto, Japan, 20-22 August 2015. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website