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Communication Dans Un Congrès Année : 2020

Using Grammar-Based Genetic Programming for Mining Disjointness Axioms Involving Complex Class Expressions

Résumé

In the context of the Semantic Web, learning implicit knowledge in terms of axioms from Linked Open Data has been the object of much current research. In this paper, we propose a method based on grammar-based genetic programming to automatically discover disjoint-ness axioms between concepts from the Web of Data. A training-testing model is also implemented to overcome the lack of benchmarks and comparable research. The acquisition of axioms is performed on a small sample of DBpedia with the help of a Grammatical Evolution algorithm. The accuracy evaluation of mined axioms is carried out on the whole DBpe-dia. Experimental results show that the proposed method gives high accuracy in mining class disjointness axioms involving complex expressions .
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Dates et versions

hal-02936193 , version 1 (11-09-2020)

Identifiants

Citer

Thu Huong Nguyen, Andrea G. B. Tettamanzi. Using Grammar-Based Genetic Programming for Mining Disjointness Axioms Involving Complex Class Expressions. ICCS 2020 - 25th International Conference on Conceptual Structures, Sep 2020, Bozen-Bolzano, Italy. pp.18-32, ⟨10.1007/978-3-030-57855-8_2⟩. ⟨hal-02936193⟩
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