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

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Book cover Genetic Programming (EuroGP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10781))

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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.

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Notes

  1. 1.

    https://www.w3.org/OWL/.

  2. 2.

    The result is a KB with an enriched expressive power. More complex relationships than subsumption can be expressed.

  3. 3.

    To guarantee decidability, only DL-safe rules are sought for [16], i.e., rules interpreted under the DL-safety condition, whose variables are bound only to explicitly named individuals in \(\mathcal {K}\). When added to an ontology, DL-safe rules are decidable and generate sound, but not necessarily complete, results.

  4. 4.

    http://www.cs.put.poznan.pl/alawrynowicz/financial.owl.

  5. 5.

    http://www.biopax.org/release/biopax-level2.owl.

  6. 6.

    http://www.semanticbible.com/ntn/ntn-view.html.

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Author information

Authors and Affiliations

  1. Université Côte d’Azur, CNRS, Inria, I3S, Sophia Antipolis, France

    Minh Duc Tran & Andrea G. B. Tettamanzi

  2. University of Bari, Bari, Italy

    Claudia d’Amato

  3. The University of Danang – University of Science and Technology, Da Nang, Vietnam

    Binh Thanh Nguyen

Authors
  1. Minh Duc Tran
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  2. Claudia d’Amato
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  3. Binh Thanh Nguyen
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  4. Andrea G. B. Tettamanzi
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Corresponding author

Correspondence to Andrea G. B. Tettamanzi .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Lisbon, Portugal

    Mauro Castelli

  2. Brno University of Technology, Brno, Czech Republic

    Lukas Sekanina

  3. Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. University of Parma, Parma, Italy

    Stefano Cagnoni

  5. University of Cádiz, Cádiz, Spain

    Pablo García-Sánchez

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Duc Tran, M., d’Amato, C., Nguyen, B.T., Tettamanzi, A.G.B. (2018). Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-relational Association Rules in the Semantic Web. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-77553-1_18

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