We propose a method to construct asymmetric metrics for evaluating the quality of multi-relational association rules coded in the form of SWRL rules. These metrics are derived from metrics for scoring association rules. We use each constructed metric as a fitness function for evolutionary inductive programming employed to discover hidden knowledge patterns (represented in SWRL) from assertional data of ontological knowledge bases. This new knowledge can be integrated easily within the ontology to enrich it. In addition, we also carry out a search for the best metric to score candidate multi-relational association rules in the evolutionary approach by experiment. We performed experiments on three publicly available ontologies validating the performances of our approach and comparing them with the main state-of-the-art systems.

Constructing metrics for evaluating multi-relational association rules in the semantic web from metrics for scoring association rules

D'Amato C.;
2019

Abstract

We propose a method to construct asymmetric metrics for evaluating the quality of multi-relational association rules coded in the form of SWRL rules. These metrics are derived from metrics for scoring association rules. We use each constructed metric as a fitness function for evolutionary inductive programming employed to discover hidden knowledge patterns (represented in SWRL) from assertional data of ontological knowledge bases. This new knowledge can be integrated easily within the ontology to enrich it. In addition, we also carry out a search for the best metric to score candidate multi-relational association rules in the evolutionary approach by experiment. We performed experiments on three publicly available ontologies validating the performances of our approach and comparing them with the main state-of-the-art systems.
978-1-5386-9313-1
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/237883
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