Abstract
Knowledge reasoning aims to infer new triples based on existing triples, which is essential for the development of large knowledge graphs, especially for knowledge graph completion. With the development of neural networks, Graph Convolutional Networks (GCNs) in knowledge reasoning have been paid widespread attention in recent years. However, the GCN model only considers the structural information of knowledge graphs and ignores the ontology semantic information. In this paper, we propose a novel model named IterG, which is able to incorporate ontology semantics seamlessly into the GCN model. More specifically, IterG learns the embeddings of knowledge graphs in an unsupervised manner via GCNs and extracts the semantic ontology information via rule learning. The model is capable of propagating relation layer-wisely as well as combining both rich structural information in knowledge graphs and ontological semantics. The experimental results on five real-world datasets demonstrate that our method outperforms the state-of-the-art approaches, and IterG can effectively and efficiently fuse ontology semantics into GCNs.
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References
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 (2008)
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, 11–15 July 2010 (2010)
Chen, D., Socher, R., Manning, C.D., Ng, A.Y.: Learning new facts from knowledge bases with neural tensor networks and semantic word vectors. arXiv preprint arXiv:1301.3618 (2013)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844–3852 (2016)
Felfernig, A., et al.: Persuasive recommendation: serial position effects in knowledge-based recommender systems. In: de Kort, Y., IJsselsteijn, W., Midden, C., Eggen, B., Fogg, B.J. (eds.) PERSUASIVE 2007. LNCS, vol. 4744, pp. 283–294. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77006-0_34
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1263–1272. JMLR. org (2017)
Jang, S., Choi, J., Yi, M.: Semi-automatic quality assessment of linked data without requiring ontology (2015)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs (2015)
Paulheim, H., Bizer, C.: Improving the quality of linked data using statistical distributions. Int. J. Semant. Web Inf. Syst. 10(2), 63–86 (2014)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Shi, B., Weninger, T.: ProjE: embedding projection for knowledge graph completion. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
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., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2007)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a large ontology from wikipedia and wordnet. J. Web Semant. 6(3), 203–217 (2008)
Suda, M., Weidenbach, C., Wischnewski, P.: On the saturation of YAGO. In: Giesl, J., Hähnle, R. (eds.) IJCAR 2010. LNCS (LNAI), vol. 6173, pp. 441–456. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14203-1_38
Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: The 3rd Workshop on Continuous Vector Space Models and their Compositionality (2015)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. International Conference on Machine Learning (ICML) (2016)
Wang, W.Y., Mazaitis, K., Cohen, W.W.: Programming with personalized pagerank: a locally groundable first-order probabilistic logic (2013)
Wang, W.Y., Mazaitis, K., Lao, N., Mitchell, T., Cohen, W.W.: Efficient inference and learning in a large knowledge base: Reasoning with extracted information using a locally groundable first-order probabilistic logic. Comput. Sci. (2014)
Weston, J., et al.: Towards AI-complete question answering: a set of prerequisite toy tasks. Comput. Sci. (2015)
Yang, B., Yih, W.t., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)
Zhang, W., et al.: Iteratively learning embeddings and rules for knowledge graph reasoning. In: The World Wide Web Conference, pp. 2366–2377 (2019)
Zhou, G., Su, J., Zhang, J., Zhang, M.: Exploring various knowledge in relation extraction. In: ACL, Meeting of the Association for Computational Linguistics, Conference, June, University of Michigan, USA (2005)
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This work is supported by the National Natural Science Foundation of China (61972275).
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Liang, X., Zhang, F., Liu, X., Yang, Y. (2020). IterG: An Iteratively Learning Graph Convolutional Network with Ontology Semantics. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_18
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