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:1.4420

@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