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Conference Papers Year : 2019

An Evolutionary Approach to Class Disjointness Axiom Discovery

Abstract

Axiom learning is an essential task in enhancing the quality of an ontology, a task that sometimes goes under the name of ontology enrichment. To overcome some limitations of recent work and to contribute to the growing library of ontology learning algorithms, we propose an evolutionary approach to automatically discover axioms from the abundant RDF data resource of the Semantic Web. We describe a method applying an instance of an Evolutionary Algorithm, namely Grammatical Evolution, to the acquisition of OWL class dis-jointness axioms, one important type of OWL axioms which makes it possible to detect logical inconsistencies and infer implicit information from a knowledge base. The proposed method uses an axiom scoring function based on possibility theory and is evaluated against a Gold Standard, manually constructed by knowledge engineers. Experimental results show that the given method possesses high accuracy and good coverage.
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Dates and versions

hal-02319638 , version 1 (18-10-2019)

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Thu Huong Nguyen, Andrea G. B. Tettamanzi. An Evolutionary Approach to Class Disjointness Axiom Discovery. WI 2019 - IEEE/WIC/ACM International Conference on Web Intelligence, Oct 2019, Thessaloniki, Greece. pp.68-75, ⟨10.1145/3350546.3352502⟩. ⟨hal-02319638⟩
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