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Disambiguating Open IE: Identifying Semantic Similarity in Relation Extraction by Word Embeddings

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11122)

Abstract

Open Information Extraction (Open IE) methods enable the extraction of structured relations from domain-independent unstructured sources. However, due to lexical variation and polysemy, we argue it is necessary to understand the meaning of an extracted relation, rather than just extracting its textual structure. In the present work, we investigate different methods for associating relations extracted by Open IE systems with the semantic relations they describe by using word embedding models. The results presented in our experiments indicate that the methods are ill-suited for this problem and show that there is still a lot to research on the Relation Disambiguation in Portuguese.

Keywords

Relation Disambiguation Open Information Extraction Semantic relations 

Notes

Acknowledgements

This study was partially funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and by Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB).

References

  1. 1.
    Apache Software Foundation: Apache jena: a free and open source java framework for building semantic web and linked data applications (2018). https://jena.apache.org/
  2. 2.
    Attardi, G.: Wikipedia extractor (2016). http://medialab.di.unipi.it/wiki/Wikipedia_Extractor
  3. 3.
    Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, Hyderabad, pp. 2670–2676 (2007)Google Scholar
  4. 4.
    Barchi, P.H., Hruschka, E.R.: Never-ending ontology extension through machine reading. In: Hybrid Intelligent Systems (HIS), pp. 266–272. IEEE (2014).  https://doi.org/10.1109/HIS.2014.7086210
  5. 5.
    Deeplearning4j DT: Deeplearning4j: open-source distributed deep learning for the JVM (2017). http://deeplearning4j.org
  6. 6.
    Frank, E., Hall, M.A., Witten, I.H.: The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann, Online (2016). https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf
  7. 7.
    Gamallo, P., Garcia, M.: Multilingual open information extraction. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds.) EPIA 2015. LNCS (LNAI), vol. 9273, pp. 711–722. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23485-4_72CrossRefGoogle Scholar
  8. 8.
    Gamallo, P., Garcia, M., Fernandez-Lanza, S.: Dependency-based open information extraction. In: Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, pp. 10–18. Association for Computational Linguistics, Stroudsburg (2012)Google Scholar
  9. 9.
    Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-match: an algorithm and an implementation of semantic matching. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 61–75. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-25956-5_5CrossRefGoogle Scholar
  10. 10.
    Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Silva, J., Aluísio, S.: Portuguese word embeddings: Evaluating on word analogies and natural language tasks. In: Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology, pp. 122–131 (2017)Google Scholar
  11. 11.
    Hasegawa, T., Sekine, S., Grishman, R.: Discovering relations among name dentities from large corpora. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 415. Association for Computational Linguistics (2004)Google Scholar
  12. 12.
    Jacquemin, B., Brun, C., Roux, C.: Enriching a text by semantic disambiguation for information extraction. In: Proceeding of the Workshop on Using Semantics for Information Retrieval and Filtering: State of the Art and Future Research (LREC 2002), Las Palmas, Canary Islands, Spain, pp. 45–51 (2002). http://arxiv.org/abs/cs/0506048
  13. 13.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977). http://www.jstor.org/stable/2529310CrossRefGoogle Scholar
  14. 14.
    Lassen, T., Terney, T.V.: An ontology-based approach to disambiguation of semantic relations. In: Proceedings of the Workshop on Learning Structured Information in Natural Language Applications (2006)Google Scholar
  15. 15.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  16. 16.
    Mohamed, T.P., Hruschka Jr., E.R., Mitchell, T.M.: Discovering relations between noun categories. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pp. 1447–1455. Association for Computational Linguistics, Stroudsburg (2011)Google Scholar
  17. 17.
    Nimishakavi, M., Singh, U.S., Talukdar, P.: Relation schema induction using tensor factorization with side information. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 414–423. Association for Computational Linguistics, Austin (2016). https://aclweb.org/anthology/D16-1040
  18. 18.
    Oliveira, L.S., Glauber, R., Claro, D.B.: Dependentie: An open information extraction system on portuguese by a dependence analysis. In: Encontro Nacional de Inteligência Artificial e Inteligência Computacional. Sociedade Brasileira de Computação (SBC), Uberlandia (2017)Google Scholar
  19. 19.
    Pereira, V., Pinheiro, V.: Report-um sistema de extração de informações aberta para língua portuguesa. In: Proceedings of the 10th Brazilian Symposium in Information and Human Language Technology, pp. 191–200. Sociedade Brasileira de Computação, Natal (2015)Google Scholar
  20. 20.
    Subhashree, S., Kumar, P.S.: Enriching linked datasets with new object properties. CoRR abs/1606.07572 (2016). http://arxiv.org/abs/1606.07572
  21. 21.
    TODA MATÉRIA: Filósofos pré-socráticos.https://www.todamateria.com.br/filosofos-pre-socraticos/. Accessed 6 Apr 2018
  22. 22.
    Trillo, C.D.P.: Recuperação de vídeos indexados por conceitos. Master’s thesis, Universidade de São Paulo, São Paulo, March 2005. https://www.ime.usp.br/~rmcobe/onair/files/christian_thesis.pdf
  23. 23.
    Van Diggelen, J., Beun, R.J., Dignum, F., Van Eijk, R.M., Meyer, J.J.: Ontology negotiation: goals, requirements and implementation. Int. J. Agent-Oriented Softw. Eng. 1(1), 63–90 (2007)CrossRefGoogle Scholar
  24. 24.
    W3C: Owl web ontology language overview (2004). http://www.w3.org/TR/2004/REC-owl-features-20040210/
  25. 25.
    Wu, F., Weld, D.S.: Open information extraction using wikipedia. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 118–127. Association for Computational Linguistics, Stroudsburg (2010). http://dl.acm.org/citation.cfm?id=1858681.1858694
  26. 26.
    Xavier, C.C., Lima, V.L.S., Souza, M.: Open information extraction based on lexical semantics. J. Braz. Comput. Soc. 21(4) (2015).  https://doi.org/10.1186/s13173-015-0023-2

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Federal University of BahiaSalvadorBrazil

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