A Candidate Generation Algorithm for Named Entities Disambiguation Using DBpedia

  • Wissem Bouarroudj
  • Zizette Boufaida
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)


Word Sense Disambiguation (WSD) is the process of choosing one sense to an ambiguous word in a context. Ambiguity refers to the fact that a word can have different meanings. One form of the lexical ambiguity is polysemy (Apple is the company and eventually the fruit). The state-of-art approaches generally extract named entities (NE), generate candidate entities from a Knowledge Base (KB), and apply a comparison method to select the correct one. As a complement to the majority of those approaches which do not use the NE categories, we propose a disambiguation algorithm that uses those categories to reduce the number of the candidates. For instance, categories include person, location, organization, etc. we will show that considering them will considerably reduce the number of the resulting candidates. In this paper, we will focus on the step of generating the candidate entities from a KB, thus we will propose an algorithm that will use DBpedia to link NE categories to the values of rdf:type property. The obtained results are very promising.


Linked Data Disambiguation Named entity Query processing Entity Linking 


  1. 1.
    Besançon, R., Hani, D., Olivier, F., Hervé, L.B.: Utilisation des relations d’une base de connaissances pour la désambiguïsation d’entités nommées. In: JEP-TALN-RECITAL, vol. 2, pp. 290–303 (2016)Google Scholar
  2. 2.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. In: Semantic Services, Interoperability and Web Applications: Emerging Concepts, pp. 205–227 (2009)Google Scholar
  3. 3.
    Hirst, G.: Semantic Interpretation and the Resolution of Ambiguity. Cambridge University Press, New York (1987)CrossRefGoogle Scholar
  4. 4.
    Höffner, K., Walter, S., Marx, E., Usbeck, R., Lehmann, J., Ngonga Ngomo, A.C.: Survey on challenges of question answering in the semantic web. Seman. Web 8(6), 895–920 (2017)CrossRefGoogle Scholar
  5. 5.
    Hulpuş, I., Prangnawarat, N., Hayes, C.: Path-based semantic relatedness on linked data and its use to word and entity disambiguation. In: International Semantic Web Conference, pp. 442–457. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  6. 6.
    Ide, N., Véronis, J.: Mapping dictionaries: a spreading activation approach. In: 6th Annual Conference of the Centre for the New Oxford English Dictionary, pp. 52–64 (1990)Google Scholar
  7. 7.
    Ide, N., Véronis, J.: Introduction to the special issue on word sense disambiguation: the state of the art. Comput. linguist. 24(1), 2–40 (1998)Google Scholar
  8. 8.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., Van Kleef, P., Auer, S., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Seman. Web 6(2), 167–195 (2015)Google Scholar
  9. 9.
    Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th Annual International Conference on Systems Documentation, pp. 24–26. ACM (1986)Google Scholar
  10. 10.
    Masterman, M.: Semantic message detection for machine translation, using an interlingua. In: Proceedings of 1961 International Conference on Machine Translation, pp. 438–475 (1961)Google Scholar
  11. 11.
    Mitkov, R.: The Oxford Handbook of Computational Linguistics. Oxford University Press, Oxford (2005)zbMATHGoogle Scholar
  12. 12.
    Ruback, L., Casanova, M.A., Renso, C., Lucchese, C.: Selector: discovering similar entities on linked data by ranking their features. In: 2017 IEEE 11th International Conference on Semantic Computing (ICSC), pp. 117–124. IEEE (2017)Google Scholar
  13. 13.
    Schütze, H.: Automatic word sense discrimination. Comput. linguist. 24(1), 97–123 (1998)MathSciNetGoogle Scholar
  14. 14.
    Small, S.L.: Word expert parsing: a theory of distributed word-based natural language understanding. Ph.D. thesis, College Park, MD, USA (1980)Google Scholar
  15. 15.
    Sussna, M.: Word sense disambiguation for free-text indexing using a massive semantic network. In: Proceedings of the Second International Conference on Information and Knowledge Management, pp. 67–74. ACM (1993)Google Scholar
  16. 16.
    Usbeck, R., Ngomo, A.C.N., Röder, M., Gerber, D., Coelho, S.A., Auer, S., Both, A.: AGDISTIS-graph-based disambiguation of named entities using linked data. In: International Semantic Web Conference, pp. 457–471. Springer, Heidelberg (2014)Google Scholar
  17. 17.
    Wilks, Y.A.: Grammar, Meaning and the Machine Analysis of Language. Routledge & Kegan Paul, London (1972)Google Scholar
  18. 18.
    Yarowsky, D.: Word-sense disambiguation using statistical models of Roget’s categories trained on large corpora. In: Proceedings of the 14th Conference on Computational Linguistics, vol. 2, pp. 454–460. Association for Computational Linguistics (1992)Google Scholar
  19. 19.
    Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics, pp. 189–196. Association for Computational Linguistics (1995)Google Scholar
  20. 20.
    Yin, X., Shah, S.: Building taxonomy of web search intents for name entity queries. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1001–1010. ACM (2010)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LIRE LaboratoryAbdelhamid Mehri Constantine 2 UniversityConstantineAlgeria

Personalised recommendations