Constructing Metrics for Evaluating Multi-Relational Association Rules in the Semantic Web from Metrics for Scoring Association Rules - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Constructing Metrics for Evaluating Multi-Relational Association Rules in the Semantic Web from Metrics for Scoring Association Rules

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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.
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hal-02084977 , version 1 (30-03-2019)

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  • HAL Id : hal-02084977 , version 1

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Minh Duc Tran, Claudia d'Amato, Andrea G. B. Tettamanzi, Binh Thanh Nguyen. Constructing Metrics for Evaluating Multi-Relational Association Rules in the Semantic Web from Metrics for Scoring Association Rules. IEEE-RIVF 2019 - International Conference on Computing and Communication Technologies, Mar 2019, Da Nang, Vietnam. pp.65-70. ⟨hal-02084977⟩
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