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Using Grammar-Based Genetic Programming for Mining Disjointness Axioms Involving Complex Class Expressions
Abstract : 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 .
Cited literature [16 references]
https://hal.inria.fr/hal-02936193
Contributor : Andrea G. B. Tettamanzi Connect in order to contact the contributor
Submitted on : Friday, September 11, 2020 - 9:46:04 AM
Last modification on : Friday, January 21, 2022 - 3:12:28 AM
Long-term archiving on: : Friday, December 4, 2020 - 5:26:53 PM
Contributor : Andrea G. B. Tettamanzi Connect in order to contact the contributor
Submitted on : Friday, September 11, 2020 - 9:46:04 AM
Last modification on : Friday, January 21, 2022 - 3:12:28 AM
Long-term archiving on: : Friday, December 4, 2020 - 5:26:53 PM
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