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

  • Minh Duc Tran
  • Claudia d’Amato
  • Binh Thanh Nguyen
  • Andrea G. B. TettamanziEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10781)


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.


Evolutionary inductive programming Description logics Semantic Web 


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the International Conference on Management of Data, pp. 207–216. ACM Press (1993)Google Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York (2003)zbMATHGoogle Scholar
  3. 3.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American (2001)Google Scholar
  4. 4.
    Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and regression trees, New York, USA (1984)Google Scholar
  5. 5.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of 1997 ACM SIGMOD International Conference on Management of Data, pp. 255–264 (1997)Google Scholar
  6. 6.
    Clark, P., Boswell, R.: Rule induction with CN2: some recent improvements. In: Proceedings of the Fifth European Conference, pp. 151–163 (1991)Google Scholar
  7. 7.
    d’Amato, C., Staab, S., Tettamanzi, A., Tran, D.M., Gandon, F.: Ontology enrichment by discovering multi-relational association rules from ontological knowledge bases. In: Proceedings of SAC 2016. ACM (2016)Google Scholar
  8. 8.
    d’Amato, C., Tettamanzi, A.G.B., Minh, T.D.: Evolutionary discovery of multi-relational association rules from ontological knowledge bases. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 113–128. Springer, Cham (2016). CrossRefGoogle Scholar
  9. 9.
    Divina, F.: Genetic Relational Search for Inductive Concept Learning: A Memetic Algorithm for ILP. LAP LAMBERT Academic Publishing (2010)Google Scholar
  10. 10.
    Fanizzi, N., d’Amato, C., Esposito, F.: Learning with kernels in description logics. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 210–225. Springer, Heidelberg (2008). CrossRefGoogle Scholar
  11. 11.
    Fu, L.M., Shortliffe, E.H.: The application of certainty factors to neural computing for rule discovery. IEEE Trans. Neural Netw. 11, 647–657 (2000)CrossRefGoogle Scholar
  12. 12.
    Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: WWW 2013, pp. 413–422. ACM (2013)Google Scholar
  13. 13.
    Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: a semantic web rule language combining OWL and RuleML (2004).
  14. 14.
    Józefowska, J., Lawrynowicz, A., Lukaszewski, T.: The role of semantics in mining frequent patterns from knowledge bases in description logics with rules. Theory Pract. Logic Program. 10(3), 251–289 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Lisi, F.A.: AL-QuIn: an onto-relational learning system for semantic web mining. Int. J. Semant. Web Inf. Syst. 7(3), 1–22 (2011)Google Scholar
  16. 16.
    Motik, B., Sattler, U., Studer, R.: Query answering for OWL-DL with rules. Web Semantics 3(1), 41–60 (2005)CrossRefGoogle Scholar
  17. 17.
    Muggleton, S., Tamaddoni-Nezhad, A.: QG/GA: a stochastic search for progol. Mach. Learn. 70(2–3), 121–133 (2008)CrossRefGoogle Scholar
  18. 18.
    Reiser, P., Riddle, P.: Scaling up inductive logic programming: an evolutionary wrapper approach. Appl. Intell. 15(3), 181–197 (2001)CrossRefzbMATHGoogle Scholar
  19. 19.
    Sahar, S., Mansour, Y.: An empirical evaluation of objective interestingness criteria. In: SPIE Conference on Data mining and Knowledge Discovery, pp. 63–74 (1999)Google Scholar
  20. 20.
    Smyth, P., Goodman, R.: Rule Induction Using Information Theory. MIT Press, Cambridge (1991)Google Scholar
  21. 21.
    Tran, M.D., d’Amato, C., Nguyen, B.T., Tettamanzi, A.G.B.: An evolutionary algorithm for discovering multi-relational association rules in the semantic web. In: GECCO, pp. 513–520. ACM (2017)Google Scholar
  22. 22.
    Völker, J., Niepert, M.: Statistical schema induction. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011. LNCS, vol. 6643, pp. 124–138. Springer, Heidelberg (2011). CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Université Côte d’Azur, CNRS, Inria, I3SSophia AntipolisFrance
  2. 2.University of BariBariItaly
  3. 3.The University of Danang – University of Science and TechnologyDa NangVietnam

Personalised recommendations