Cluster Computing

, Volume 20, Issue 2, pp 969–977 | Cite as

Knowledge entity learning and representation for ontology matching based on deep neural networks

Article

Abstract

We study the task of ontology matching that is used mainly for solving the semantic heterogeneity problems, which concentrates on finding semantically related entities between different ontologies. Many previous works exploit the character-level or token-level information of the descriptions of an entity in ontology directly when applying the string-based matcher or token based matcher to find the corresponding entities. They ignored the higher level correlations between different descriptions of an entity. To address this problem, we propose a representation learning method based on deep neural networks which aim at learning the high level abstract representations of the input entity. Particularly, the representations of the entities are learned in an unsupervised way firstly, and then fine-tuned in a supervised manner with the training data. The experiment results show that our approaches can learn useful representations for entities from its descriptive information to better measure the similarity between entities.

Keywords

Ontology matching Deep neural networks Semantic web 

Notes

Acknowledgements

This research work has been supported by the National Nature Science Foundation of China (No. 61672553) and the Ministry of Education Humanities Social Sciences Research Projects (No. 16YJCZH076).

References

  1. 1.
    Zhang, C., Hoffmann, R., Weld, D. S.: Ontological smoothing for relation extraction with minimal supervision. In: AAAI (2012)Google Scholar
  2. 2.
    Liu, W., Luo, X., Gong, Z., Xuan, J., Kou, N., Xu, Z.: Discovering the core semantics of event from social media. Fut. Gen. Comput. Syst. 64, 175–185 (2015)CrossRefGoogle Scholar
  3. 3.
    Xu, Z., Wei, X., Liu, Y., Mei, L., Hu, C., Choo, K., Zhu, Y., Sugumaran, V.: Building the search pattern of web users using conceptual semantic space model. Int. J. Web Grid Serv. 12(3), 328–347 (2016)Google Scholar
  4. 4.
    Wang, X., Zhang, H., Xu, Z.: Public sentiments analysis based on fuzzy logic for text. Int. J. Softw. Eng. Knowl. Eng. 26(9–10), 1341–1360 (2016)CrossRefGoogle Scholar
  5. 5.
    Cruz, I.F., Xiao, H., Hsu, F.: An ontology-based framework for XML semantic integration, pp. 217–226. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  6. 6.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)MATHGoogle Scholar
  7. 7.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)CrossRefGoogle Scholar
  8. 8.
    Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst. 19, 153 (2007)Google Scholar
  9. 9.
    Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length, and helmholtz free energy. In: Advances in Neural Information Processing Systems, pp. 3–3. Morgan Kaufmann, San Francisco (1994)Google Scholar
  10. 10.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)MATHGoogle Scholar
  11. 11.
    Mao, M., Peng, Y., Spring, M.: An adaptive ontology mapping approach with neural network based constraint satisfaction. Web Semant. Sci. Serv. Agents World Wide Web 8(1), 14–25 (2010)CrossRefGoogle Scholar
  12. 12.
    Mao, M., Peng, Y., Spring, M.: Ontology mapping: as a binary classification problem. Concurr. Comput. Pract. Exp. 23(9), 1010–1025 (2011)CrossRefGoogle Scholar
  13. 13.
    Ngo, D., Bellahsene, Z.: Yam++: a multi-strategy based approach for ontology matching task. In: Knowledge Engineering and Knowledge Management, pp. 421–425. Springer, Berlin (2012)Google Scholar
  14. 14.
    Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: Proceedings of 18th International Conference on Data Engineering, pp. 117–128. IEEE, Washington (2002)Google Scholar
  15. 15.
    Pirr’o, G., Talia, D.: Ufome: an ontology mapping system with strategy prediction capabilities. Data Knowl. Eng. 69(5), 444–471 (2010)CrossRefGoogle Scholar
  16. 16.
    Ji, Q., Haase, P., Qi, G.: Combination of similarity measures in ontology matching using the owa operator. In: Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice, pp. 281–295. Springer, Berlin (2011)Google Scholar
  17. 17.
    Jean-Mary, Y.R., Shironoshita, E.P., Kabuka, M.R.: Ontology matching with semantic verification. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 235–251 (2009)CrossRefGoogle Scholar
  18. 18.
    Doan, A., Madhavan, J., Dhamankar, R., Domingos, P., Halevy, A.: Learning to match ontologies on the semantic web. VLDB J. 12(4), 303–319 (2003)CrossRefGoogle Scholar
  19. 19.
    Peng, Y., Munro, P.W., Mao, M.: Learning to map ontologies with neural network. In: OM (2009)Google Scholar
  20. 20.
    Bordes, A., Weston, J., Collobert, R., Bengio, Y. et al.: Learning structured embeddings of knowledge bases. In: AAAI (2011)Google Scholar
  21. 21.
    Bourlard, H., Kamp, Y.: Autoassociation by multilayer perceptrons and singular value decomposition. Biol. Cybernet. 59(4–5), 291–294 (1988)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Coates, A., Ng, A.Y., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: International Conference on Artificial Intelligence and Statistics, pp. 215–223 (2011)Google Scholar
  23. 23.
    Erhan, D., Bengio, Y., Courville, A., Manzagol, P.-A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625–660 (2010)MathSciNetMATHGoogle Scholar
  24. 24.
    Ngo, D., Bellahsene, Z., Coletta, R.: A flexible system for ontology matching. In: IS Olympics: Information Systems in a Diverse World, pp. 79–94. Springer, Berlin (2012)Google Scholar
  25. 25.
    Cheatham, M., Hitzler, P.: String similarity metrics for ontology alignment. In: The Semantic Web—ISWC 2013, pp. 294–309. Springer, Heidelberg (2013)Google Scholar
  26. 26.
    Cohen, W.W., Ravikumar, P.D., Fienberg, S.E., et al.: A comparison of string distance metrics for name-matching tasks. In: IIWeb-2003, pp. 73–78 (2003)Google Scholar
  27. 27.
    Lin, F., Sandkuhl, K.: A survey of exploiting wordnet in ontology matching. In: Artificial Intelligence in Theory and Practice II, pp. 341–350. Springer, Heidelberg (2008)Google Scholar
  28. 28.
    Stoilos, G., Stamou, G., Kollias, S.: A string metric for ontology alignment. In: The Semantic Web—ISWC 2005, pp. 624–637. Springer, Heidelberg (2005)Google Scholar
  29. 29.
    Mao, M., Peng, Y., Spring, M.: A profile propagation and information retrieval based ontology mapping approach. In: Third International Conference on Semantics, Knowledge and Grid, pp. 164–169. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  30. 30.
    Ngo, D., Bellahsene, Z., Coletta, R.: A generic approach for combining linguistic and context profile metrics in ontology matching. In: On the Move to Meaningful Internet Systems: OTM 2011, pp. 800–807. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Lirong Qiu
    • 1
  • Jia Yu
    • 1
  • Qiumei Pu
    • 1
  • Chuncheng Xiang
    • 1
  1. 1.Department of Information TechnologyMinzu University of ChinaBeijingChina

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