Inherited Properties of \(\mathcal {FL}_0\) Concept Similarity Measure Under Preference Profile

  • Teeradaj RacharakEmail author
  • Satoshi Tojo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11352)


Measuring concept similarity in ontologies is central to the functioning of many techniques such as ontology matching, ontology learning, and many related applications in the bio-medical domain. Generally, it can be seen as a generalization of concept equivalence problem in Description Logics. That is, any two concepts are equivalent if and only if their similarity degree is one. The recently introduced measures can be used to identify such kind of similarity degree between \(\mathcal {FL}_0\) concept descriptions not only w.r.t. the objective factors (e.g. the structure of concept descriptions) but also w.r.t. the subjective factors called preference profile (e.g. the agent’s preferences). In this paper, we provide proofs of theorems about their inherited properties including their relationship to the classical reasoning problem of concept equivalence.


Concept similarity measure Semantic web ontology Preference profile Description Logics 



This work is supported by the Japan Society for the Promotion of Science (JSPS kaken no. 17H02258) and is part of the JAIST-NECTEC-SIIT dual doctoral degree program. The authors would also like to thank the editors for the comments.


  1. 1.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation and Applications, 2nd edn. Cambridge University Press, New York (2010)zbMATHGoogle Scholar
  2. 2.
    Ashburner, M., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)CrossRefGoogle Scholar
  3. 3.
    Euzenat, J., Valtchev, P.: Similarity-based ontology alignment in OWL-lite. In: de Mántaras, R.L., Saitta, L. (eds.) Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004), pp. 333–337. IOS Press (2004)Google Scholar
  4. 4.
    Raha, S., Hossain, A., Ghosh, S.: Similarity based approximate reasoning: fuzzy control. J. Appl. Log. 6(1), 47–71 (2008)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Sessa, M.I.: Approximate reasoning by similarity-based SLD resolution. Theor. Comput. Sci. 275(1–2), 389–426 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Racharak, T., Tojo, S., Hung, N.D., Boonkwan, P.: Argument-based logic programming for analogical reasoning. In: Kurahashi, S., Ohta, Y., Arai, S., Satoh, K., Bekki, D. (eds.) JSAI-isAI 2016. LNCS (LNAI), vol. 10247, pp. 253–269. Springer, Cham (2017). Scholar
  7. 7.
    Racharak, T., Tojo, S., Hung, N.D., Boonkwan, P.: Combining answer set programming with description logics for analogical reasoning under an agent’s preferences. In: Benferhat, S., Tabia, K., Ali, M. (eds.) IEA/AIE 2017. LNCS (LNAI), vol. 10351, pp. 306–316. Springer, Cham (2017). Scholar
  8. 8.
    Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327–352 (1977)CrossRefGoogle Scholar
  9. 9.
    Lehmann, K., Turhan, A.-Y.: A Framework for semantic-based similarity measures for \(\cal{ELH}\)-concepts. In: del Cerro, L.F., Herzig, A., Mengin, J. (eds.) JELIA 2012. LNCS (LNAI), vol. 7519, pp. 307–319. Springer, Heidelberg (2012). Scholar
  10. 10.
    Tongphu, S., Suntisrivaraporn, B.: Algorithms for measuring similarity between \({\cal{ELH}}\) concept descriptions: a case study on SNOMED CT. J. Comput. Inform. 36, 733–764 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Racharak, T., Suntisrivaraporn, B., Tojo, S.: \({\sf sim}^\pi \): a concept similarity measure under an agent’s preferences in description logic \({\cal{ELH}}\). In: Proceedings of the 8th International Conference on Agents and Artificial Intelligence, pp. 480–487 (2016)Google Scholar
  12. 12.
    Racharak, T., Suntisrivaraporn, B., Tojo, S.: Personalizing a concept similarity measure in the description logic \({\cal{ELH}}\) with preference profile. J. Comput. Inform. 37, 581–613 (2018)CrossRefGoogle Scholar
  13. 13.
    Racharak, T., Tojo, S.: Concept similarity under the agent’s preferences for the description logic \(\cal{FL}_0\) with unfoldable TBox. In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence, pp. 201–210 (2018)Google Scholar
  14. 14.
    Baader, F., Narendran, P.: Unification of concept terms in description logics. J. Symb. Comput. 31(3), 277–305 (2001)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Brachman, R.J., Levesque, H.J.: The tractability of subsumption in frame-based description languages. In: AAAI, vol. 84, pp. 34–37 (1984)Google Scholar
  16. 16.
    Racharak, T., Suntisrivaraporn, B., Tojo, S.: Identifying an agent’s preferences toward similarity measures in description logics. In: Qi, G., Kozaki, K., Pan, J.Z., Yu, S. (eds.) JIST 2015. LNCS, vol. 9544, pp. 201–208. Springer, Cham (2016). 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.
    Caviedes, J.E., Cimino, J.J.: Towards the development of a conceptual distance metric for the UMLS. J. Biomed. Inform. 37(2), 77–85 (2004)CrossRefGoogle Scholar
  19. 19.
    Ge, J., Qiu, Y.: Concept similarity matching based on semantic distance. In: Proceedings of the 4th International Conference on Semantics, Knowledge and Grid, pp. 380–383, December 2008Google Scholar
  20. 20.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, vol. 1, pp. 448–453. Morgan Kaufmann Publishers Inc., San Francisco (1995)Google Scholar
  21. 21.
    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
  22. 22.
    Patwardhan, S.: Using WordNet-based context vectors to estimate the semantic relatedness of concepts. In: Proceedings of the EACL 2006 Workshop Making Sense of Sense-bringing Computational Linguistics and Psycholinguistics Together, vol. 1501, pp. 1–8 (2006)Google Scholar
  23. 23.
    Schütze, H.: Automatic word sense discrimination. Comput. Linguist. 24(1), 97–123 (1998)MathSciNetGoogle Scholar
  24. 24.
    Lai, K.H., Topaz, M., Goss, F.R., Zhou, L.: Automated misspelling detection and correction in clinical free-text records. J. Biomed. Inform. 55, 188–195 (2015)CrossRefGoogle Scholar
  25. 25.
    Gabrys, R., Yaakobi, E., Milenkovic, O.: Codes in the Damerau distance for DNA storage. In: 2016 IEEE International Symposium on Information Theory (ISIT), pp. 2644–2648. IEEE (2016)Google Scholar
  26. 26.
    Bille, P.: A survey on tree edit distance and related problems. Theor. Comput. Sci. 337(1–3), 217–239 (2005)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpeset des jura. Bull. de la Societe Vaudoise des Sci. Nat. 37, 547–579 (1901)Google Scholar
  28. 28.
    Suntisrivaraporn, B.: A similarity measure for the description logic EL with unfoldable terminologies, pp. 408–413 (2013)Google Scholar
  29. 29.
    Suntisrivaraporn, B., Tongphu, S.: A structural subsumption based similarity measure for the description logic \(\cal{ALEH}\). In: Proceedings of the 8th International Conference on Agents and Artificial Intelligence, ICAART 2016, pp. 204–212. SCITEPRESS - Science and Technology Publications, Lda (2016)Google Scholar
  30. 30.
    Racharak, T., Suntisrivaraporn, B.: Similarity measures for \(\cal{FL}_0\) concept descriptions from an automata-theoretic point of view. In: Proceedings of the 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), pp. 1–6, March 2015Google Scholar
  31. 31.
    D’Amato, C., Fanizzi, N., Esposito, F.: A semantic similarity measure for expressive description logics. CoRR abs/0911.5043 (2009)Google Scholar
  32. 32.
    d’Amato, C., Staab, S., Fanizzi, N.: On the influence of description logics ontologies on conceptual similarity. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 48–63. Springer, Heidelberg (2008). Scholar
  33. 33.
    Alsubait, T., Parsia, B., Sattler, U.: Measuring conceptual similarity in ontologies: how bad is a cheap measure? In: Informal Proceedings of the 27th International Workshop on Description Logics, Vienna, Austria, 17–20 July 2014, pp. 365–377 (2014)Google Scholar
  34. 34.
    Bernstein, A., Kaufmann, E., Bürki, C., Klein, M.: How similar is it? Towards personalized similarity measures in ontologies. In: Ferstl, O.K., Sinz, E.J., Eckert, S., Isselhorst, T. (eds.) Wirtschaftsinformatik 2005: eEconomy, eGovernment, eSociety, pp. 1347–1366. Physica-Verlag HD, Heidelberg (2005). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Information, Computer, and Communication Technology, Sirindhorn International Institute of TechnologyThammasat UniversityPathum ThaniThailand
  2. 2.School of Information ScienceJapan Advanced Institute of Science and TechnologyIshikawaJapan

Personalised recommendations