Abstract
Knowledge graph (KG) is a structured semantic knowledge base, which is widely used in the fields of semantic search, such as intelligent Q&A and intelligent recommendation. Representation learning, as a key issue of KG, aims to vectorize entities and relations in KG to reduce data sparseness and improve computational efficiency. Translation-based representation learning model shows great knowledge representation ability, but there also are limitations in complex relations modeling and representation accuracy. To address these problems, this paper proposes a novel representation learning model with semantic vectors, called TransV, which makes full use of external text corpus and KG’s context to accurately represent entities and complex relations. Entity semantic vectors and relation semantic vectors are constructed, which can not only deeply extend semantic structure of KG, but also transform complex relations into precise simple relations from a semantic perspective. Link prediction and triple classification tasks are performed on TransV with public datasets. Experimental results show that TransV can outperform other translation-based models. Mean Rank is reduced by 66 and Hits@10 is increased by 20% on average for link prediction task on FB15K.
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References
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38, 39–41 (1995)
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 (2008)
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)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015)
Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 2659–2665 (2016)
Lv, X., Hou, L., Li, J., Liu, Z.: Differentiating concepts and instances for knowledge graph embedding. In: Proceedings of the Empirical Methods in Natural Language Processing, pp. 1971–1979 (2018)
He, S., Liu, K., Ji, G., Zhao, J.: Learning to represent knowledge graphs with gaussian embedding. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 623–632 (2015)
Zhang, W., Paudel, B., Zhang, W., Bernstein, A., Chen, H.: Interaction embeddings for prediction and explanation in knowledge graphs. In: Proceedings of the ACM International Conference on Web Search and Data Mining, pp. 96–104 (2019)
Wang, Z., Li, J., Liu, Z., Tang, J.: Text-enhanced representation learning for knowledge graph. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 1293–1299 (2016)
Xie, R., Liu, Z., Sun, M.: Representation learning of knowledge graphs with hierarchical types. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 2965–2971 (2016)
Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: Proceedings of Empirical Methods Natural Language Process, pp. 705–714 (2015)
An, B., Chen, B., Han, X., Sun, L.: Accurate text-enhanced knowledge graph representation learning. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 745–755 (2018)
Nie, B., Sun, S.: Joint knowledge base embedding with neighborhood context. In: Proceedings of the 24th International Conference on Pattern Recognition, pp. 379–384 (2018)
Guan, N., Song, D., Liao, L.: Knowledge graph embedding with concepts. Knowl.-Based Syst. 164, 38–44 (2019)
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence, pp. 301–306 (2011)
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of Advances in Neural Information Processing Systems, pp. 926–934 (2013)
Ferragina, P., Scaiella, U.: Tagme: on-the-fly annotation of short text fragments. In: Proceedings of the 19th ACM Conference on Information and Knowledge Management, pp. 1625–1628 (2010)
Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. In: Proceedings of the 6th ACM Conference on Conference on Information & Knowledge Management (2007)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: The North American Chapter of the Association for Computational Linguistics, pp. 4171–4186 (2018)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Acknowledgements
The work was supported by the National Natural Science Foundation of China (NO. 61976193), the Science and Technology Key Research Planning Project of Zhejiang Province (NO. 2021C03136), and the Natural Science Foundation of Zhejiang Province (No. LY19F020034).
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Gao, T., Zhang, Y., Li, M., Lu, J., Cheng, Z., Xiao, G. (2021). Representation Learning of Knowledge Graph with Semantic Vectors. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_2
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