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Enhancing Text-Based Relatedness Measures with Semantic Web Data

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ICT Innovations 2016 (ICT Innovations 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 665))

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Abstract

Entity relatedness measures quantify the amount of association between two entities, such as people, places or events, and are fundamental part of many Natural Language Processing and Information Retrieval applications. Calculating entity relatedness requires access to entity specific information, so a very common practice is to use Wikipedia or its Semantic Web representations as source of knowledge. This paper explores which of the different semantic relationships that associate two entities in DBpedia are good indicators of their relatedness and could be used to enhance some of the standard text-based relatedness measures. The ultimate goal is learning a well performing relatedness calculation method that does not require vast amount of preprocessing, but is applicable in cases when entities lack either textual context or semantic relationships. The KORE entity relatedness dataset was used for learning a convenient and well performing method for measuring relatedness and its evaluation.

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Notes

  1. 1.

    http://wiki.dbpedia.org/.

  2. 2.

    https://www.wikipedia.org/.

  3. 3.

    http://www.mpi-inf.mpg.de/yago/.

  4. 4.

    https://www.freebase.com/.

  5. 5.

    http://wiki.dbpedia.org/services-resources/datasets/dbpedia-data-set-2014.

  6. 6.

    http://purl.org/dc/terms/subject.

  7. 7.

    http://www.w3.org/2004/02/skos/.

  8. 8.

    http://www.w3.org/1999/02/22-rdf-syntax-ns#type.

  9. 9.

    http://www.w3.org/2000/01/rdf-schema#domain.

  10. 10.

    http://www.w3.org/2000/01/rdf-schema#range.

  11. 11.

    https://en.wikipedia.org/wiki/Oracle_Corporation.

  12. 12.

    https://en.wikipedia.org/wiki/Tim_Berners-Lee.

  13. 13.

    https://en.wikipedia.org/wiki/Mont_Blanc.

References

  1. Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)

    Article  MATH  Google Scholar 

  2. Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet:: Similarity: measuring the relatedness of concepts. In: Demonstration papers at HLT-NAACL 2004, pp. 38–41. Association for Computational Linguistics (2004)

    Google Scholar 

  3. Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint cmp-lg/9511007 (1995)

    Google Scholar 

  4. Strube, M., Ponzetto, S.P.: WikiRelate! computing semantic relatedness using Wikipedia. In: AAAI, vol. 6, pp. 1419–1424 (2006)

    Google Scholar 

  5. Berners-Lee, T., Hendler, J., Lassila, O., et al.: The semantic web. Sci. Am. 284(5), 28–37 (2001)

    Article  Google Scholar 

  6. 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. Semant. Web J. 5, 1–29 (2014)

    Google Scholar 

  7. Medelyan, O., Milne, D., Legg, C., Witten, I.H.: Mining meaning from Wikipedia. Int. J. Hum.-Comput. Stud. 67(9), 716–754 (2009)

    Article  Google Scholar 

  8. Schuhmacher, M., Ponzetto, S.P.: Knowledge-based graph document modeling. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 543–552. ACM (2014)

    Google Scholar 

  9. Hulpuş, I., Prangnawarat, N., Hayes, C.: Path-based semantic relatedness on linked data and its use to word and entity disambiguation. In: The Semantic Web–ISWC 2015, pp. 442–457. Springer (2015)

    Google Scholar 

  10. Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics (1994)

    Google Scholar 

  11. Leacock, C., Chodorow, M.: Combining local context and Wordnet similarity for word sense identification. In: WordNet: An Electronic Lexical Database, vol. 49, no. 2, pp. 265–283 (1998)

    Google Scholar 

  12. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. IJCAI, vol. 7, pp. 1606–1611 (2007)

    Google Scholar 

  13. Witten, I., Milne, D.: An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In: Proceeding of AAAI Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, Chicago, USA, pp. 25–30. AAAI Press (2008)

    Google Scholar 

  14. Hoffart, J., Seufert, S., Nguyen, D.B., Theobald, M., Weikum, G.: KORE: keyphrase overlap relatedness for entity disambiguation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 545–554. ACM (2012)

    Google Scholar 

  15. Aggarwal, N., Buitelaar, P.: Wikipedia-based distributional semantics for entity relatedness. In: 2014 AAAI Fall Symposium Series (2014)

    Google Scholar 

  16. Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., Trani, S.: Learning relatedness measures for entity linking. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 139–148. ACM (2013)

    Google Scholar 

  17. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ss. Cyril and Methodius in Skopje, Skopje, Macedonia

    Ana Gjorgjevikj, Riste Stojanov & Dimitar Trajanov

Authors
  1. Ana Gjorgjevikj
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  2. Riste Stojanov
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  3. Dimitar Trajanov
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Corresponding author

Correspondence to Ana Gjorgjevikj .

Editor information

Editors and Affiliations

  1. Paris, France

    Georgi Stojanov

  2. Skopje, Macedonia

    Andrea Kulakov

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Gjorgjevikj, A., Stojanov, R., Trajanov, D. (2018). Enhancing Text-Based Relatedness Measures with Semantic Web Data. In: Stojanov, G., Kulakov, A. (eds) ICT Innovations 2016. ICT Innovations 2016. Advances in Intelligent Systems and Computing, vol 665. Springer, Cham. https://doi.org/10.1007/978-3-319-68855-8_18

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  • DOI: https://doi.org/10.1007/978-3-319-68855-8_18

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