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Ontology Matching with Word Embeddings

  • Conference paper
Book cover Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (NLP-NABD 2014, CCL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8801))

Abstract

Ontology matching is one of the most important work to achieve the goal of the semantic web. To fulfill this task, element-level matching is an indispensable step to obtain the fundamental alignment. In element-level matching process, previous work generally utilizes WordNet to compute the semantic similarities among elements, but WordNet is limited by its coverage. In this paper, we introduce word embeddings to the field of ontology matching. We testified the superiority of word embeddings and presented a hybrid method to incorporate word embeddings into the computation of the semantic similarities among elements. We performed the experiments on the OAEI benchmark, conference track and real-world ontologies. The experimental results show that in element-level matching, word embeddings could achieve better performance than previous methods.

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References

  1. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: Dbpedia: A nucleus for a web of open data. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  2. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: A collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)

    Google Scholar 

  3. Cheatham, M., Hitzler, P.: String similarity metrics for ontology alignment. In: Alani, H., et al. (eds.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 294–309. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Choi, N., Song, I.Y., Han, H.: A survey on ontology mapping. ACM Sigmod Record 35(3), 34–41 (2006)

    Article  Google Scholar 

  5. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. The Journal of Machine Learning Research 12, 2493–2537 (2011)

    MATH  Google Scholar 

  6. Cruz, I.F., Antonelli, F.P., Stroe, C.: Agreementmaker: efficient matching for large real-world schemas and ontologies. Proceedings of the VLDB Endowment 2(2), 1586–1589 (2009)

    Article  Google Scholar 

  7. Euzenat, J., Shvaiko, P., et al.: Ontology matching. Springer (2007)

    Google Scholar 

  8. Foltz, P.W.: Latent semantic analysis for text-based research. Behavior Research Methods, Instruments, & Computers 28(2), 197–202 (1996)

    Article  Google Scholar 

  9. Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: A deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 513–520 (2011)

    Google Scholar 

  10. Gruber, T.R., et al.: A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220 (1993)

    Article  Google Scholar 

  11. Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: Yago2: a spatially and temporally enhanced knowledge base from wikipedia. Artificial Intelligence 194, 28–61 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  12. Jean-Mary, Y.R., Shironoshita, E.P., Kabuka, M.R.: Ontology matching with semantic verification. Web Semantics: Science, Services and Agents on the World Wide Web 7(3), 235–251 (2009)

    Article  Google Scholar 

  13. Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. arXiv preprint cmp-lg/9709008 (1997)

    Google Scholar 

  14. Li, J., Tang, J., Li, Y., Luo, Q.: Rimom: A dynamic multistrategy ontology alignment framework. IEEE Transactions on Knowledge and Data Engineering 21(8), 1218–1232 (2009)

    Article  Google Scholar 

  15. Li, Y., McLean, D., Bandar, Z.A., O’shea, J.D., Crockett, K.: Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering 18(8), 1138–1150 (2006)

    Article  Google Scholar 

  16. Lin, D.: An information-theoretic definition of similarity. In: Proc. 15th International Conf. on Machine Learning, vol. 98, pp. 296–304 (1998)

    Google Scholar 

  17. Lin, F., Sandkuhl, K.: A survey of exploiting wordnet in ontology matching. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice II. IFIP, vol. 276, pp. 341–350. Springer, Heidelberg (2008)

    Google Scholar 

  18. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS (2013)

    Google Scholar 

  19. Mikolov, T., Zweig, G.: Context dependent recurrent neural network language model. In: SLT, pp. 234–239 (2012)

    Google Scholar 

  20. Miller, G.A.: Wordnet: a lexical database for English. Communications of the ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  21. Ngo, D., Bellahsene, Z., Todorov, K.: Opening the black box of ontology matching. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 16–30. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

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

    Google Scholar 

  23. Shvaiko, P., Euzenat, J.: Ontology matching: State of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering, 158–176 (2013)

    Google Scholar 

  24. Socher, R., Lin, C.C., Manning, C., Ng, A.Y.: Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 129–136 (2011)

    Google Scholar 

  25. Stoilos, G., Stamou, G., Kollias, S.D.: A string metric for ontology alignment. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 624–637. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  26. Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394. Association for Computational Linguistics (2010)

    Google Scholar 

  27. 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 

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Author information

Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China

    Yuanzhe Zhang, Xuepeng Wang, Siwei Lai, Shizhu He, Kang Liu & Jun Zhao

  2. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University, China

    Xueqiang Lv

Authors
  1. Yuanzhe Zhang
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  2. Xuepeng Wang
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  3. Siwei Lai
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  4. Shizhu He
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  5. Kang Liu
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  6. Jun Zhao
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  7. Xueqiang Lv
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Haidian District, 100084, Beijing, China

    Maosong Sun  & Yang Liu  & 

  2. Chinese Academy of Sciences, Institute of Automation, 100190, Beijing, China

    Jun Zhao

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Zhang, Y. et al. (2014). Ontology Matching with Word Embeddings. In: Sun, M., Liu, Y., Zhao, J. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2014 2014. Lecture Notes in Computer Science(), vol 8801. Springer, Cham. https://doi.org/10.1007/978-3-319-12277-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-12277-9_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12276-2

  • Online ISBN: 978-3-319-12277-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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