Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-Relational Association Rules in the Semantic Web

Created by W.Langdon from gp-bibliography.bib Revision:1.7964

@InProceedings{Tran:2018:EuroGP,

• author = "Minh Duc Tran and Claudia d'Amato and Binh Thanh Nguyen and Andrea G. B. Tettamanzi",
• title = "Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-Relational Association Rules in the Semantic Web",
• booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming",
• year = "2018",
• month = "4-6 " # apr,
• editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
• series = "LNCS",
• volume = "10781",
• publisher = "Springer Verlag",
• address = "Parma, Italy",
• pages = "289--305",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming: Poster",
• isbn13 = "978-3-319-77552-4",
• DOI = "doi:10.1007/978-3-319-77553-1_18",
• abstract = "We carry out a comparison of popular asymmetric metrics, originally proposed for scoring association rules, as building blocks for a fitness function for evolutionary inductive programming. In particular, we use them to score candidate multi-relational association rules in an evolutionary approach to the enrichment of populated knowledge bases in the context of the Semantic Web. The evolutionary algorithm searches for hidden knowledge patterns, in the form of SWRL rules, in assertional data, while exploiting the deductive capabilities of ontologies. Our methodology is to compare the number of generated rules and total predictions when the metrics are used to compute the fitness function of the evolutionary algorithm. This comparison, which has been carried out on three publicly available ontologies, is a crucial step towards the selection of suitable metrics to score multi-relational association rules that are generated from ontologies.",
• notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018",

}

Genetic Programming entries for Minh Duc Tran Claudia d'Amato Binh Thanh Nguyen Andrea G B Tettamanzi

Citations