Abstract
We propose a model which jointly learns the embeddings of multiple knowledge bases (KBs) in a uniform vector space to align entities in KBs. Instead of using content similarity based methods, we think the structure information of KBs is also important for KB alignment. When facing the cross-linguistic or different encoding situation, what we can leverage are only the structure information of two KBs. We utilize seed entity alignments whose embeddings are ensured the same in the joint learning process. We perform experiments on two datasets including a subset of Freebase comprising 15 thousand selected entities, and a dataset we construct from real-world large scale KBs – Freebase and DBpedia. The results show that the proposed approach which only utilize the structure information of KBs also works well.
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Notes
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In step5, 7 and 35 are empirical values chosen in experiments.
References
Akbari, I., Fathian, M., Badie, K.: An improved mlma+ and its application in ontology matching. In: Innovative Technologies in Intelligent Systems, Industrial Applications, CITISIA 2009, pp. 56–60. IEEE (2009)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). doi:10.1007/978-3-540-76298-0_52
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Conference on Artificial Intelligence, number EPFL-CONF-192344 (2011)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Chang, K.-W., Yih, W.-T., Meek, C.: Multi-relational latent semantic analysis. In: EMNLP, pp. 1602–1612 (2013)
Damak, S., Souid, H., Kachroudi, M., Zghal, S.: EXONA Results for OAEI (2015)
Gokhale, C., Das, S., Doan, A., Naughton, J.F., Rampalli, N., Shavlik, J., Zhu, X.: Hands-off crowdsourcing for entity matching. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 601–612. ACM (2014)
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of ACL, pp. 687–696 (2015)
Joslyn, C.A., Paulson, P., White, A., Al Saffar, S.: Measuring the structural preservation of semantic hierarchy alignments. In: Proceedings of the 4th International Workshop on Ontology Matching, CEUR Workshop Proceedings, vol. 551, pp. 61–72. Citeseer (2009)
Khiat, A., Benaissa, M.: InsMT+ Results for OAEI 2015 Instance Matching (2015)
Lacoste-Julien, S., Palla, K., Davies, A., Kasneci, G., Graepel, T., Ghahramani, Z.: Simple greedy matching for aligning large knowledge bases. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 572–580. ACM (2013)
Prytkova, N., Weikum, G., Spaniol, M.: Aligning multi-cultural knowledge taxonomies by combinatorial optimization. In: Proceedings of the 24th International Conference on World Wide Web Companion, pp. 93–94. International World Wide Web Conferences Steering Committee (2015)
Pushpakumar, R., Sai Baba, M., Madurai Meenachi, N., Balasubramanian, P.: Instance Based Matching System for Nuclear Ontologies (2016)
Scharffe, F., Zamazal, O., Fensel, D.: Ontology alignment design patterns. Knowl. Inf. Syst. 40(1), 1–28 (2014)
Seddiqui, M., Nath, R.P.D., Aono, M.: An efficient metric of automatic weight generation for properties in instance matching technique. arXiv preprint arXiv:1502.03556 (2015)
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934 (2013)
Suchanek, F.M., Abiteboul, S., Senellart, P.: Paris: probabilistic alignment of relations, instances, and schema. Proc. VLDB Endow. 5(3), 157–168 (2011)
Suna, Y., Maa, L., Wangb, S.: A Comparative Evaluation of String Similarity Metrics for Ontology Alignment (2015)
Wang, W., Wang, P.: Lily Results for OAEI 2015 (2015)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp. 1112–1119. Citeseer (2014)
Zhang, Y., Li, J.: RiMOM Results for OAEI 2015 (2015)
Shvaiko, P., Euzenat, J.: Ten challenges for ontology matching. In: Meersman, R., Tari, Z. (eds.) OTM 2008. LNCS, vol. 5332, pp. 1164–1182. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88873-4_18
Berstein, P.A., Madhavan, J., Rahm, E.: Generic schema matching, ten years later. Proc. VLDB Endow. 4(11), 695–701 (2011)
Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Know. Data Eng. 25(1), 158–176 (2013)
Han, J.W., Kambe, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Berlin (2000)
Kantardzic, M.: Data Mining. Wiley, Hoboken (2011)
Cohen, W.W., Richman, J.: Learning to match, cluster large high-dimensional data sets for data integration. In: Proceddings of Advances in Neural Information Processing Systems, pp. 905–912. MIT Press, Cambridge (2005)
Acknowledgement
This work was supported by the Natural Science Foundation of China (No. 61533018), the National Basic Research Program of China (No. 2014CB340503) and the National Natural Science Foundation of China (No. 61272332). And this work was also supported by Google through focused research awards program.
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Hao, Y., Zhang, Y., He, S., Liu, K., Zhao, J. (2016). A Joint Embedding Method for Entity Alignment of Knowledge Bases. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_1
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