Abstract
In the process of ontology construction, we often need to find relations between entities described by the Resource Description Framework (RDF). Predicting relations between RDF entities is important for developing large-scale ontologies. The goal of our research is to predict a relation (predicate) of two given entities (subject and object). TransE and TransR have been proposed as the methods for such a prediction. We propose a method for predicting a predicate from a subject and an object by using a Deep Neural Network (DNN), and developed RDFDNN. Experimental results showed that predictions by RDFDNN are more accurate than those by TransE and TransR.
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References
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Part Adv. Neural Inform. Process. Syst. 26, 2787–2795 (2013)
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 (SIGMOD 2008), pp. 1247–1250 (2008)
Google, “Freebase Data Dumps”. https://developers.google.com/freebase/data
Google, “Google Knowledge Graph Search API - Google Developers”. https://developers.google.com/knowledge-graph/
Guo, S., Ding, B., Wang, Q., Wang, L., Wang, B.: Knowledge base completion via rule-enhanced relational learning. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds.) CCKS 2016. CCIS, vol. 650, pp. 219–227. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-3168-7_22
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Kavalec, M., Svatek, V.: A Study on automated relation labelling in ontology learning. In: Ontology Learning from Text: Methods, Evaluation and Applications. IOS Press, Amsterdam (2003)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference for Learning Representations, Published as a Conference Paper at the 3rd International Conference for Learning Representations, San Diego (2015)
Lassila, O., Swick, R.R.: Resource Description Framework (RDF) Model and Syntax Specification. https://www.w3.org/TR/1999/REC-rdf-syntax-19990222/
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Nickel, M., Tresp, V., Kriegel, H.-P.: A Three-Way model for collective learning on Multi-Relational data. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, WA, USA, pp. 809–816 (2011)
Russakovsky, O., Deng, J., Hao, S., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)
Wang, Q., Wang, B., Guo, L.: Knowledge base completion using embeddings and rules. In: Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI 2015), pp. 1859–1865 (2015)
Wang, Q., Liu, J., Luo, Y., Wang, B., Lin, C.-Y.: Knowledge base completion via coupled path ranking. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1308–1318 (2016)
Weichselbraun, A., Wohlgenannt, G., Scharl, A.: Refining non-taxonomic relation labels with external structured data to support ontology learning. Data Knowl. Eng. 69(8), 763–778 (2010)
Acknowledgement
This work was supported by Tokyo Tech - Fuji Xerox Cooperative Research (Project Code KY260195), JSPS Grant-in-Aid for Scientific Research (B) (Grant Number 17H01785) and JST CREST (Grant Number JPMJCR1687).
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Murata, T. et al. (2017). Predicting Relations Between RDF Entities by Deep Neural Network. In: Blomqvist, E., Hose, K., Paulheim, H., Ławrynowicz, A., Ciravegna, F., Hartig, O. (eds) The Semantic Web: ESWC 2017 Satellite Events. ESWC 2017. Lecture Notes in Computer Science(), vol 10577. Springer, Cham. https://doi.org/10.1007/978-3-319-70407-4_43
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DOI: https://doi.org/10.1007/978-3-319-70407-4_43
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