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Mining the semantic Web for OWL axioms

Thu Huong Nguyen 1, 2
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In the Semantic Web era, Linked Open Data (LOD) is its most successful implementation, which currently contains billions of RDF (Resource Data Framework) triples derived from multiple, distributed, heterogeneous sources. The role of a general semantic schema, represented as an ontology, is essential to ensure the correctness and consistency in LOD and make it possible to infer implicit knowledge by reasoning. The growth of LOD creates an opportunity for the discovery of ontological knowledge from its raw RDF data itself to enrich relevant knowledge bases. In this work, we aim at discovering schema-level knowledge in the form of axioms encoded in OWL (Ontology Web Language) from RDF data. The approaches to automated generation of the axioms from recorded RDF facts on the Web may be regarded as a case of inductive reasoning and ontology learning. The instances, represented by RDF triples, play the role of specific observations, from which axioms can be extracted by generalization.Based on the insight that discovering new knowledge is essentially an evolutionary process, whereby hypotheses are generated by some heuristic mechanism and then tested against the available evidence, so that only the best hypotheses survive, we propose a model applying Grammatical Evolution, one type of evolutionary algorithm, to mine OWL axioms from an RDF data repository. In addition, we specialize the model for the specific problem of learning OWL class disjointness axioms, along with the experiments performed on DBpedia, one of the prominent examples of LOD. Furthermore, we use different axiom scoring functions based on possibility theory, which are well-suited to the open world assumption scenario of LOD, to evaluate the quality of discovered axioms. Specifically, we proposed a set of measures to build objective functions based on single-objective and multi-objective models, respectively. Finally, in order to validate it, the performance of our approach is evaluated against subjective and objective benchmarks, and is also compared to the main state-of-the-art systems.
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Submitted on : Thursday, October 28, 2021 - 9:24:28 AM
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Thu Huong Nguyen. Mining the semantic Web for OWL axioms. Web. Université Côte d'Azur, 2021. English. ⟨NNT : 2021COAZ4052⟩. ⟨tel-03406784⟩

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