Dealing with Incompatibilities During a Knowledge Bases Fusion Process

  • Fabien Amarger
  • Jean-Pierre Chanet
  • Ollivier Haemmerlé
  • Nathalie Hernandez
  • Catherine Roussey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9717)

Abstract

More and more data sets are published on the linked open data. Reusing these data is a challenging task as for a given domain, several data sets built for specific usage may exist. In this article we present an approach for existing knowledge bases fusion by taking into account incompatibilities that may appear in their representations. Equivalence mappings established by an alignment tool are considered in order to generate a subset of compatible candidates. The approach has been evaluated by domain experts on datasets dealing with agriculture.

Keywords

Knowledge acquisition Knowledge base fusion Incompatibilities 

References

  1. 1.
    Amarger, F., Chanet, J.-P., Haemmerlé, O., Hernandez, N., Roussey, C.: SKOS sources transformations for ontology engineering: agronomical taxonomy use case. In: Closs, S., Studer, R., Garoufallou, E., Sicilia, M.-A. (eds.) MTSR 2014. CCIS, vol. 478, pp. 314–328. Springer, Heidelberg (2014)Google Scholar
  2. 2.
    Amarger, F., Chanet, J.-P., Haemmerlé, O., Hernandez, N., Roussey, C.: Construction d’une ontologie par transformation de systèmes d’organisation des connaissances et évaluation de la confiance. Ingénierie des Systèmes d’Information 20(3), 37–61 (2015)CrossRefGoogle Scholar
  3. 3.
    Bron, C., Kerbosch, J.: Algorithm 457: finding all cliques of an undirected graph. Commun. ACM 16, 575–577 (1973)CrossRefMATHGoogle Scholar
  4. 4.
    Dragisic, Z., Eckert, K., Euzenat, J., Faria, D., Ferrara, A., Granada, R., Ivanova, V., Jiménez-Ruiz, E., Kempf, A.O., Lambrix, P., et al.: Results of the ontology alignment evaluation initiative 2014 (2014)Google Scholar
  5. 5.
    Eppstein, D., Strash, D.: Listing all maximal cliques in large sparse real-world graphs. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 364–375. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Jiménez-Ruiz, E., Cuenca Grau, B.: LogMap: logic-based and scalable ontology matching. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 273–288. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Raunich, S., Rahm, E.: Towards a benchmark for ontology merging. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds.) OTM-WS 2012. LNCS, vol. 7567, pp. 124–133. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Roussey, C., Chanet, J.P., Cellier, V., Amarger, F.: Agronomic taxon. In: WOD, p. 5 (2013)Google Scholar
  9. 9.
    Tomita, E., Tanaka, A., Takahashi, H.: The worst-case time complexity for generating all maximal cliques and computational experiments. Theoret. Comput. Sci. 363, 28–42 (2006)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fabien Amarger
    • 2
  • Jean-Pierre Chanet
    • 1
  • Ollivier Haemmerlé
    • 2
  • Nathalie Hernandez
    • 2
  • Catherine Roussey
    • 1
  1. 1.Irstea, UR TSCF Technologies et systèmes d’information pour les agrosystèmesAubièreFrance
  2. 2.IRIT, UMR 5505, Université Toulouse – Jean JaurèsToulouse CedexFrance

Personalised recommendations