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Knowledge and Information Systems

, Volume 59, Issue 1, pp 33–65 | Cite as

DIS-C: conceptual distance in ontologies, a graph-based approach

  • Rolando QuinteroEmail author
  • Miguel Torres-Ruiz
  • Rolando Menchaca-Mendez
  • Marco A. Moreno-Armendariz
  • Giovanni Guzman
  • Marco Moreno-Ibarra
Regular Paper
  • 64 Downloads

Abstract

This paper presents the DIS-C approach, which is a novel method to assess the conceptual distance between concepts within an ontology. DIS-C is graph based in the sense that the whole topology of the ontology is considered when computing the weight of the relationships between concepts. The methodology is composed of two main steps. First, in order to take advantage of previous knowledge, an expert of the ontology domain assigns initial weight values to each of the relations in the ontology. Then, an automatic method for computing the conceptual relations refines the weights assigned to each relation until reaching a stable state. We introduce a metric called generality that is defined in order to evaluate the accessibility of each concept, considering the ontology like a strongly connected graph. Unlike most previous approaches, the DIS-C algorithm computes similarity between concepts in ontologies that are not necessarily represented in a hierarchical or taxonomic structure. So, DIS-C is capable of incorporating a wide variety of relationships between concepts such as meronymy, antonymy, functionality and causality.

Keywords

Conceptual distance Semantic similarity Ontology Graph 

Notes

Acknowledgements

Work partially sponsored by Instituto Politécnico Nacional and SIP-IPN under Grants 20182159, 20180308, 20180409, 20180773, 20180839 and 20181568. Also is sponsored by Consejo Nacional de Ciencia y Tecnología (CONACyT) under Grant PN-2016/2110. We are thankful to the reviewers for their invaluable and constructive feedback that helped improve the quality of this paper.

References

  1. 1.
    Al-Mubaid H, Nguyen H et al (2006) A cluster-based approach for semantic similarity in the biomedical domain. In: Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th annual international conference of the IEEE’, IEEE, pp 2713–2717Google Scholar
  2. 2.
    Al-Mubaid H, Nguyen H et al (2009) Measuring semantic similarity between biomedical concepts within multiple ontologies. IEEE Trans Syst Man Cybern Part C: Appl Rev 39(4):389–398CrossRefGoogle Scholar
  3. 3.
    Albacete E, Calle-Gómez J, Castro E, Cuadra D (2012) Semantic similarity measures applied to an ontology for human-like interaction. J Artif Intell Res (JAIR) 44:397–421CrossRefzbMATHGoogle Scholar
  4. 4.
    Albertoni R, De Martino M (2006) Semantic similarity of ontology instances tailored on the application context. In: On the move to meaningful internet systems 2006: CoopIS, DOA, GADA, and ODBASE, Springer, Berlin, pp 1020–1038Google Scholar
  5. 5.
    Atkinson J, Ferreira A, Aravena E (2009) Discovering implicit intention-level knowledge from natural-language texts. Knowl-Based Syst 22(7):502–508CrossRefGoogle Scholar
  6. 6.
    Batet M, Sánchez D, Valls A (2011) An ontology-based measure to compute semantic similarity in biomedicine. J Biomed Inform 44(1):118–125CrossRefGoogle Scholar
  7. 7.
    Blanco-Fernández Y, Pazos-Arias JJ, Gil-Solla A, Ramos-Cabrer M, López-Nores M, García-Duque J, Fernández-Vilas A, Díaz-Redondo RP, Bermejo-Muñoz J (2008) A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems. Knowl-Based Syst 21(4):305–320CrossRefGoogle Scholar
  8. 8.
    Bollegala D, Matsuo Y, Ishizuka M (2007) Measuring semantic similarity between words using web search engines. WWW 7:757–766Google Scholar
  9. 9.
    Budan I, Graeme H (2006) Evaluating wordnet-based measures of semantic distance. Comut Linguist 32(1):13–47CrossRefzbMATHGoogle Scholar
  10. 10.
    Chu H-C, Chen M-Y, Chen Y-M (2009) A semantic-based approach to content abstraction and annotation for content management. Expert Syst Appl 36(2):2360–2376CrossRefGoogle Scholar
  11. 11.
    Cilibrasi RL, Vitanyi P (2007) The google similarity distance. IEEE Trans Knowl Data Eng 19(3):370–383CrossRefGoogle Scholar
  12. 12.
    Consortium GO (2004) The gene ontology (go) database and informatics resource. Nucleic Acids Res 32(suppl 1):D258–D261CrossRefGoogle Scholar
  13. 13.
    Couto FM, Silva MJ, Coutinho PM (2007) Measuring semantic similarity between gene ontology terms. Data Knowl Eng 61(1):137–152CrossRefGoogle Scholar
  14. 14.
    Cross V, Hu X (2011) Using semantic similarity in ontology alignment. Ontology Matching p 61Google Scholar
  15. 15.
    Ding L, Finin T, Joshi A, Pan R, Cost RS, Peng Y, Reddivari P, Doshi V, Sachs J (2004) Swoogle: a search and metadata engine for the semantic web. In: Proceedings of the thirteenth ACM international conference on Information and knowledge management, ACM, 652–659Google Scholar
  16. 16.
    Fellbaum C (1998) WordNet: an electronic database. MIT Press, CambridgeCrossRefzbMATHGoogle Scholar
  17. 17.
    Fonseca F (2008) Ontology-based geospatial data integration. In: Encyclopedia of GIS, pp 812–815Google Scholar
  18. 18.
    Formica A (2006) Ontology-based concept similarity in formal concept analysis. Inf Sci 176(18):2624–2641MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Gangemi A, Guarino N, Masolo C, Oltramari A, Schneider L (2002) Sweetening ontologies with dolce. In: Knowledge engineering and knowledge management: ontologies and the semantic web. Springer, Berlin, pp 166–181Google Scholar
  20. 20.
    Goldstone R (1994a) An efficient method for obtaining similarity data. Behav Res Methods Instrum Comput 26(4):381–386CrossRefGoogle Scholar
  21. 21.
    Goldstone RL (1994b) Similarity, interactive activation, and mapping. J Exp Psychol Learn Mem Cognit 20(1):3CrossRefGoogle Scholar
  22. 22.
    Goldstone RL, Medin DL, Halberstadt J (1997) Similarity in context. Mem Cognit 25(2):237–255CrossRefGoogle Scholar
  23. 23.
    Han L, Sun L, Chen G, Xie L (2006) Adss: an approach to determining semantic similarity. Adv Eng Softw 37(2):129–132CrossRefGoogle Scholar
  24. 24.
    Harispe S, Sánchez D, Ranwez S, Janaqi S, Montmain J (2014) A framework for unifying ontology-based semantic similarity measures: a study in the biomedical domain. J Biomed Inform 48:38–53CrossRefGoogle Scholar
  25. 25.
    Héja G, Surján G, Varga P (2008) Ontological analysis of snomed ct. BMC Med Inform Decis Mak 8(Suppl 1):S8CrossRefGoogle Scholar
  26. 26.
    Hirst G, St-Onge D (1998) Lexical chains as representations of context for the detection and correction of malapropisms. WordNet: Electron Lex Database 305:305–332Google Scholar
  27. 27.
    Hliaoutakis A, Varelas G, Voutsakis E, Petrakis EG, Milios E (2006) Information retrieval by semantic similarity. Int J Semant Web Inf Syst 2(3):55–73CrossRefGoogle Scholar
  28. 28.
    Jain P, Yeh PZ, Verma K, Vasquez RG, Damova M, Hitzler P, Sheth AP (2011) Contextual ontology alignment of lod with an upper ontology: a case study with proton. In: The semantic web: research and applications. Springer, Berlin, pp 80–92Google Scholar
  29. 29.
    Janowicz K, Raubal M, Kuhn W (2015) The semantics of similarity in geographic information retrieval. J Spat Inf Sci 2:29–57Google Scholar
  30. 30.
    Jarmasz M, Szpakowicz S (2003) Roget’s thesaurus and semantic similarity. In: Proceedings of the international conference on recent advances in natural language processing, 212–219Google Scholar
  31. 31.
    Jiang JJ, Conrath DW (1997) Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of the international conference on research in computational linguistics, 19–33Google Scholar
  32. 32.
    Kashyap V, Sheth A (1996) Semantic and schematic similarities between database objects: a context-based approach. VLDB J-Int J Very Large Data Bases 5(4):276–304CrossRefGoogle Scholar
  33. 33.
    Kastrati Z, Imran AS, Yildirim-Yayilgan S (2016) Semcon: a semantic and contextual objective metric for enriching domain ontology concepts. Int J Semant Web Inf Syst 12(2):1–24CrossRefGoogle Scholar
  34. 34.
    Kumar S, Baliyan N, Sukalikar S (2017) Ontology cohesion and coupling metrics. Int J Semant Web Inf Syst 13(4):1–26CrossRefGoogle Scholar
  35. 35.
    Leacock C, Chodorow M (1998) Combining local context and wordnet similarity for word sense identification. WordNet: Electron Lex Database 49(2):265–283Google Scholar
  36. 36.
    Levachkine S, Guzmán-Arenas A (2007) Hierarchy as a new data type for qualitative variables. Expert Syst Appl 32(3):899–910CrossRefGoogle Scholar
  37. 37.
    Li Y, Bandar Z, McLean D et al (2003) An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans Knowl Data Eng 15(4):871–882CrossRefGoogle Scholar
  38. 38.
    Li Y, McLean D, Bandar Z, O’shea JD, Crockett K (2006) Sentence similarity based on semantic nets and corpus statistics. IEEE Trans Knowl Data Eng 18(8):1138–1150CrossRefGoogle Scholar
  39. 39.
    Likavec S, Osborne F, Cena F (2015) Property-based semantic similarity and relatedness for improving recommendation accuracy and diversity. Int J Semant Web Inf Syst 11(4):1–40CrossRefGoogle Scholar
  40. 40.
    Lin D et al (1998) An information-theoretic definition of similarity. In: ICML vol 98, 296–304Google Scholar
  41. 41.
    Meilicke C, Stuckenschmidt H, Tamilin A (2007) Repairing ontology mappings. In: AAAI, vol 3, 6Google Scholar
  42. 42.
    Meng L, Huang R, Gu J (2013) A review of semantic similarity measures in wordnet. Int J Hybrid Inf Technol 6(1):1–12Google Scholar
  43. 43.
    Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41CrossRefGoogle Scholar
  44. 44.
    Miller GA, Charles WG (1991) Contextual correlates of semantic similarity. Lang Cognit Process 6(1):1–28MathSciNetCrossRefGoogle Scholar
  45. 45.
    Moreno M (2007) Similitud semantica entre sistemas de objetos geograficos aplicada a la generalizacion de datos geo-espaciales, Ph.D. thesisGoogle Scholar
  46. 46.
    Nedas K, Egenhofer M (2008) Spatial-scene similarity queries. Trans GIS 12(6):661–681CrossRefGoogle Scholar
  47. 47.
    Niles I, Pease A (2001) Towards a standard upper ontology. In: Proceedings of the international conference on formal ontology in information systems, 2001, ACM, 2–9Google Scholar
  48. 48.
    Patwardhan S, Banerjee S, Pedersen T (2003) Using measures of semantic relatedness for word sense disambiguation. In: Computational linguistics and intelligent text processing. Springer, Berlin, 241–257Google Scholar
  49. 49.
    Pedersen T, Pakhomov SV, Patwardhan S, Chute CG (2007) Measures of semantic similarity and relatedness in the biomedical domain. J Biomed Inform 40(3):288–299CrossRefGoogle Scholar
  50. 50.
    Petrakis EG, Varelas G, Hliaoutakis A, Raftopoulou P (2006) X-similarity: computing semantic similarity between concepts from different ontologies. JDIM 4(4):233–237Google Scholar
  51. 51.
    Pirró G (2009) A semantic similarity metric combining features and intrinsic information content. Data Knowl Eng 68(11):1289–1308CrossRefGoogle Scholar
  52. 52.
    Pirrò G, Ruffolo M, Talia D (2009) Secco: on building semantic links in peer-to-peer networks. In: Journal on data semantics XII’, Springer, Berlin, 1–36Google Scholar
  53. 53.
    Rada R, Mili H, Bicknell E, Blettner M (1989) Development and application of a metric on semantic nets. IEEE Trans Syst Man Cybern 19(1):17–30CrossRefGoogle Scholar
  54. 54.
    Resnik P (1995) Using information content to evaluate semantic similarity in a taxonomy, arXiv preprint cmp-lg/9511007Google Scholar
  55. 55.
    Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 11:95–130CrossRefzbMATHGoogle Scholar
  56. 56.
    Rissland EL (2006) Ai and similarity. IEEE Intell Syst 3:39–49CrossRefGoogle Scholar
  57. 57.
    Rodríguez MA, Egenhofer MJ (2003) Determining semantic similarity among entity classes from different ontologies. IEEE Trans Knowl Data Eng 15(2):442–456CrossRefGoogle Scholar
  58. 58.
    Rodríguez M, Egenhofer M (2004) Comparing geospatial entity classes: an asymmetric and context-dependent similarity measure. Int J Geogr Inf Sci 18(3):229–256CrossRefGoogle Scholar
  59. 59.
    Rubenstein H, Goodenough JB (1965) Contextual correlates of synonymy. Commun ACM 8(10):627–633CrossRefGoogle Scholar
  60. 60.
    Sánchez D (2010) A methodology to learn ontological attributes from the web. Data Knowl Eng 69(6):573–597CrossRefGoogle Scholar
  61. 61.
    Sánchez D, Batet M (2011) Semantic similarity estimation in the biomedical domain: an ontology-based information-theoretic perspective. J Biomed Inform 44(5):749–759CrossRefGoogle Scholar
  62. 62.
    Sánchez D, Batet M (2013) A semantic similarity method based on information content exploiting multiple ontologies. Expert Syst Appl 40(4):1393–1399CrossRefGoogle Scholar
  63. 63.
    Sánchez D, Batet M, Isern D (2011) Ontology-based information content computation. Knowl-Based Syst 24(2):297–303CrossRefGoogle Scholar
  64. 64.
    Sánchez D, Batet M, Isern D, Valls A (2012) Ontology-based semantic similarity: a new feature-based approach. Expert Syst Appl 39(9):7718–7728CrossRefGoogle Scholar
  65. 65.
    Sánchez D, Isern D (2011) Automatic extraction of acronym definitions from the web. Appl Intell 34(2):311–327CrossRefGoogle Scholar
  66. 66.
    Sánchez D, Isern D, Millan M (2011) Content annotation for the semantic web: an automatic web-based approach. Knowl Inf Syst 27(3):393–418CrossRefGoogle Scholar
  67. 67.
    Sánchez D, Moreno A, Del Vasto-Terrientes L (2012) Learning relation axioms from text: an automatic web-based approach. Expert Syst Appl 39(5):5792–5805CrossRefGoogle Scholar
  68. 68.
    Sánchez D, Solé-Ribalta A, Batet M, Fz Serratosa (2012) Enabling semantic similarity estimation across multiple ontologies: an evaluation in the biomedical domain. J Biomed Inform 45(1):141–155CrossRefGoogle Scholar
  69. 69.
    Saruladha K, Aghila G, Bhuvaneswary A (2011) Information content based semantic similarity for cross ontological concepts. Int J Eng Sci Technol 3(6)Google Scholar
  70. 70.
    Schickel-Zuber V, Faltings B (2007) Oss: a semantic similarity function based on hierarchical ontologies. In: IJCAI, vol 7, 551–556Google Scholar
  71. 71.
    Schwering A (2005) Hybrid model for semantic similarity measurement. In: On the move to meaningful internet systems 2005: CoopIS, DOA, and ODBASE’, Springer, Berlin, 1449–1465Google Scholar
  72. 72.
    Schwering A (2008) Approaches to semantic similarity measurement for geo-spatial data: a survey. Trans GIS 12(1):5–29CrossRefGoogle Scholar
  73. 73.
    Schwering A, Raubal M (2005) Measuring semantic similarity between geospatial conceptual regions. In: GeoSpatial semantics. Springer, Berlin, 90–106Google Scholar
  74. 74.
    Seco N, Veale T, Hayes J (2004) An intrinsic information content metric for semantic similarity in wordnet. In: ECAI, vol 16, 1089Google Scholar
  75. 75.
    Sheeren D, Mustière S, Zucker JD (2009) A data mining approach for assessing consistency between multiple representations in spatial databases. Int J Geogr Inf Sci 23:961–992CrossRefGoogle Scholar
  76. 76.
    Sinha R, Mihalcea R (2007) Unsupervised graph-basedword sense disambiguation using measures of word semantic similarity. In: Null, IEEE, 363–369Google Scholar
  77. 77.
    Song W, Li CH, Park SC (2009) Genetic algorithm for text clustering using ontology and evaluating the validity of various semantic similarity measures. Expert Syst Appl 36(5):9095–9104CrossRefGoogle Scholar
  78. 78.
    Stevenson M, Greenwood MA (2005) A semantic approach to ie pattern induction. In: Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics, 379–386Google Scholar
  79. 79.
    Tapeh AG, Rahgozar M (2008) A knowledge-based question answering system for b2c ecommerce. Knowl-Based Syst 21(8):946–950CrossRefGoogle Scholar
  80. 80.
    Torres M, Quintero R, Moreno-Ibarra M, Menchaca-Mendez R, Guzman G (2011) GEONTO-MET: an approach to conceptualizing the geographic domain. Int J Geogr Inf Sci 25(10):1633–1657CrossRefGoogle Scholar
  81. 81.
    Tversky A, Gati I (1978) Studies of similarity. Cognit Categ 1(1978):79–98Google Scholar
  82. 82.
    Wang H, Wang W, Yang J, Yu PS (2002) Clustering by pattern similarity in large data sets. In: Proceedings of the 2002 ACM SIGMOD international conference on management of data. ACM, 394–405Google Scholar
  83. 83.
    Wu Z, Palmer M (1994) Verbs semantics and lexical selection. In: Proceedings of the 32nd annual meeting on association for computational linguistics. Association for Computational Linguistics, 133–138Google Scholar
  84. 84.
    Zadeh PDH, Reformat MZ (2013) Assessment of semantic similarity of concepts defined in ontology. Inf Sci 250:21–39CrossRefGoogle Scholar
  85. 85.
    Zhou Z, Wang Y, Gu J (2008) A new model of information content for semantic similarity in wordnet. In: Future generation communication and networking symposia, 2008. FGCNS’08. Second international conference on’, vol 3, IEEE, 85–89Google Scholar

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Authors and Affiliations

  1. 1.Instituto Politécnico Nacional - Centro de Investigación en ComputaciónUPALM-ZacatencoMexico CityMexico

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