Klarman, SBritz, K2016-02-232016-02-232015-07Klarman, S and Britz, K. 2015. Towards unsupervised ontology learning from data. In: International Workshop on Defeasible and Ampliative Reasoning (DARe-15), Buenos Aires, Argentina, July 2015http://ceur-ws.org/Vol-1423/DARe-15_5.pdfhttp://ceur-ws.org/Vol-1423/http://hdl.handle.net/10204/8414International Workshop on Defeasible and Ampliative Reasoning (DARe-15), Buenos Aires, Argentina, 25-27 July 2015Data-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 EL and define corresponding learning algorithms.enOntologiesOntology LearningDescription logic TBoxesDescription logic ELFinite Learning SetsLearning AlgorithmsLearning ModelConference PresentationKlarman, S., & Britz, K. (2015). Towards unsupervised ontology learning from data. CEUR-WS. http://hdl.handle.net/10204/8414Klarman, S, and K Britz. "Towards unsupervised ontology learning from data." (2015): http://hdl.handle.net/10204/8414Klarman S, Britz K, Towards unsupervised ontology learning from data; CEUR-WS; 2015. http://hdl.handle.net/10204/8414 .TY - Conference Presentation AU - Klarman, S AU - Britz, K AB - 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 EL and define corresponding learning algorithms. DA - 2015-07 DB - ResearchSpace DP - CSIR KW - Ontologies KW - Ontology Learning KW - Description logic TBoxes KW - Description logic EL KW - Finite Learning Sets KW - Learning Algorithms KW - Learning Model LK - https://researchspace.csir.co.za PY - 2015 T1 - Towards unsupervised ontology learning from data TI - Towards unsupervised ontology learning from data UR - http://hdl.handle.net/10204/8414 ER -