Advertisement

A Particle Swarm-Based Approach for Semantic Similarity Computation

  • Samira BabalouEmail author
  • Alsayed Algergawy
  • Birgitta König-Ries
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10574)

Abstract

Semantic similarity plays a vital role within a myriad of shared data applications, such as data and information integration. A first step towards building such applications is to determine concepts, which are semantically similar to each other. One way to compute this similarity of two concepts is to assess their word similarity by exploiting different knowledge sources, e.g., ontologies, thesauri, domain corpora, etc. Over the last few years, several approaches to similarity assessment based on quantifying information content of concepts have been proposed and have shown encouraging performance. For all these approaches, the Least Common Subsumer (LCS) of two concepts plays an important role in determining their similarity. In this paper, we investigate the influence the choice of this node (or a set of nodes) on the quality of the similarity assessment. In particular, we develop a particle swarm optimization approach that optimally discovers LCSs. An empirical evaluation, based on well-established biomedical benchmarks and ontologies, illustrates the accuracy of the proposed approach, and demonstrates that similarity estimations provided by our approach are significantly more correlated with human ratings of similarity than those obtained via related works.

Keywords

Semantic web Biology ontology Semantic similarity Particle swarm optimization 

Notes

Acknowledgments

The work has been (partly) funded by the Deutsche Forschungsgemeinschaft (DFG) as part of CRC 1076 AquaDiva. S. Babalou is also supported by a scholarship from German Academic Exchange Service (DAAD).

References

  1. 1.
    Al-Mubaid, H., Nguyen, H.A.: Measuring semantic similarity between biomedical concepts within multiple ontologies. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 39(4), 389–398 (2009)CrossRefGoogle Scholar
  2. 2.
    Batet, M., Harispe, S., Ranwez, S., Sánchez, D., Ranwez, V.: An information theoretic approach to improve semantic similarity assessments across multiple ontologies. Info. Sci. 283, 197–210 (2014)CrossRefGoogle Scholar
  3. 3.
    Batet, M., Sánchez, D., Valls, A., Gibert, K.: Semantic similarity estimation from multiple ontologies. Appl. Intell. 38(1), 29–44 (2013)CrossRefGoogle Scholar
  4. 4.
    Bock, J., Hettenhausen, J.: Discrete particle swarm optimisation for ontology alignment. Inf. Sci. 192, 152–173 (2012)CrossRefGoogle Scholar
  5. 5.
    Correa, E.S., Freitas, A.A., Johnson, C.G.: A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 35–42. ACM (2006)Google Scholar
  6. 6.
    Correa, E.S., Freitas, A.A., Johnson, C.G.: Particle swarm and Bayesian networks applied to attribute selection for protein functional classification. In: Proceedings of the 9th Annual Conference on Companion on Genetic and Evolutionary Computation, pp. 2651–2658. ACM (2007)Google Scholar
  7. 7.
    Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies. Bioinformatics 30(5), 740–742 (2013)CrossRefGoogle Scholar
  8. 8.
    Hliaoutakis, A.: Semantic similarity measures in mesh ontology and their application to information retrieval on medline. Master’s thesis (2005)Google Scholar
  9. 9.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. arXiv preprint cmp-lg/9709008 (1997)
  10. 10.
    Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. WordNet Electron. Lexical Database 49(2), 265–283 (1998)Google Scholar
  11. 11.
    Lin, D., et al.: An information-theoretic definition of similarity. In: ICML, vol. 98, pp. 296–304. Citeseer (1998)Google Scholar
  12. 12.
    Martı, S., Valls, A., SáNchez, D., et al.: Semantically-grounded construction of centroids for datasets with textual attributes. Knowl.-Based Syst. 35, 160–172 (2012)CrossRefGoogle Scholar
  13. 13.
    Nelson, S.J., Johnston, W.D., Humphreys, B.L.: Relationships in medical subject headings (MeSH). In: Bean, C.A., Green, R. (eds.) Relationships in the Organization of Knowledge. Information Science and Knowledge Management, vol. 2, pp. 171–184. Springer, Dordrecht (2001). doi: 10.1007/978-94-015-9696-1_11 CrossRefGoogle Scholar
  14. 14.
    Patwardhan, S., Banerjee, S., Pedersen, T.: Using measures of semantic relatedness for word sense disambiguation. In: Gelbukh, A. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 241–257. Springer, Heidelberg (2003). doi: 10.1007/3-540-36456-0_24 CrossRefGoogle Scholar
  15. 15.
    Pedersen, T., Pakhomov, S.V., Patwardhan, S., Chute, C.G.: Measures of semantic similarity and relatedness in the biomedical domain. J. Biomed. Inform. 40(3), 288–299 (2007)CrossRefGoogle Scholar
  16. 16.
    Petrakis, E.G., Varelas, G., Hliaoutakis, A., Raftopoulou, P.: X-similarity: computing semantic similarity between concepts from different ontologies. JDIM 4(4), 233–237 (2006)Google Scholar
  17. 17.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19(1), 17–30 (1989)CrossRefGoogle Scholar
  18. 18.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint cmp-lg/9511007 (1995)
  19. 19.
    Rodríguez, M.A., Egenhofer, M.J.: Determining semantic similarity among entity classes from different ontologies. IEEE Trans. Knowl. Data Eng. 15(2), 442–456 (2003)CrossRefGoogle Scholar
  20. 20.
    Sánchez, D., Batet, M.: A new model to compute the information content of concepts from taxonomic knowledge. Int. J. Semant. Web Info. Syst. (IJSWIS) 8(2), 34–50 (2012)CrossRefGoogle Scholar
  21. 21.
    Sánchez, D., Batet, M.: A semantic similarity method based on information content exploiting multiple ontologies. Expert Syst. Appl. 40(4), 1393–1399 (2013)CrossRefGoogle Scholar
  22. 22.
    Sánchez, D., Batet, M., Isern, D., Valls, A.: Ontology-based semantic similarity: a new feature-based approach. Expert Syst. Appl. 39(9), 7718–7728 (2012)CrossRefGoogle Scholar
  23. 23.
    Sánchez, D., Solé-Ribalta, A., Batet, M., Serratosa, F.: Enabling semantic similarity estimation across multiple ontologies: an evaluation in the biomedical domain. J. Biomed. Inform. 45(1), 141–155 (2012)CrossRefGoogle Scholar
  24. 24.
    Saruladha, K., Aghila, G., Bhuvaneswary, A.: Information content based semantic similarity for cross ontological concepts. Int. J. Eng. Sci. Tech. 3(6), 327–336 (2011)Google Scholar
  25. 25.
    Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in WordNet. In: Proceedings of the 16th European Conference on Artificial Intelligence, pp. 1089–1090. IOS Press (2004)Google Scholar
  26. 26.
    Spackman, K.: SNOMED CT milestones: endorsements are added to already-impressive standards credentials. Healthc. Inf. Bus. Mag. info. Commun. Syst. 21(9), 54–56 (2004)Google Scholar
  27. 27.
    Sy, M.-F., Ranwez, S., Montmain, J., Regnault, A., Crampes, M., Ranwez, V.: User centered and ontology based information retrieval system for life sciences. BMC Bioinform. 13, S4 (2012)CrossRefGoogle Scholar
  28. 28.
    Vicient, C., Sánchez, D., Moreno, A.: An automatic approach for ontology-based feature extraction from heterogeneous textualresources. Eng. Appl. Artif. Intell. 26(3), 1092–1106 (2013)CrossRefGoogle Scholar
  29. 29.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138 (1994)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Samira Babalou
    • 1
    Email author
  • Alsayed Algergawy
    • 1
  • Birgitta König-Ries
    • 1
  1. 1.Heinz-Nixdorf Chair for Distributed Information SystemsFriedrich Schiller University of JenaJenaGermany

Personalised recommendations