Abstract
Knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph construction, the complexity of unstructured text and the incorrect text may make automatic construction tools unable to accurately obtain the semantic information in the text. This leads to high-quality noise with matched entity types but semantic errors. Currently knowledge representation learning methods assume that the knowledge in knowledge graphs is completely correct, and ignore the noise data generated in the process of automatic construction of knowledge graphs, resulting in errors in the vector representation of entities and relations. In order to reduce the negative impact of noise data on the construction of a representation learning model, in this study, a high-quality noise detection method with rule information is proposed. Based on the semantic association between triples in the same rule, we propose the concept of rule-based triple confidence. The calculation strategy of triple confidence is designed inspired by probabilistic soft logic (PSL). The influence of high-quality noise data in the training process of the model can be weakened by this confidence. Experiments show the effectiveness of the proposed method in dealing with high-quality noise.
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References
Wu, X., Chen, H., Wu, G., Liu, J., Zheng, Q., He, X., et al.: Knowledge engineering with big data. IEEE Intell. Syst. 30(5), 46–55 (2015)
Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 601–610 (2014)
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)
Lehmann, J., Isele, R., Jakob, M., et al.: DBpedia – a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web 6(2), 167–195 (2015)
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)
Bordes, A., Weston, J., Usunier, N.: Open question answering with weakly supervised embedding models. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 165–180. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44848-9_11
Saxena, A., Tripathi, A., Talukdar, P.: Improving multi-hop question answering over knowledge graphs using knowledge base embeddings. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4498–4507 (2020)
Zheng, Z., Si, X., Li, F., Chang, E. Y., Zhu, X.: Entity disambiguation with freebase. In: IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 82–89 (2012)
Jiang, T., Bu, C., Zhu, Y., Wu, X.: Two-stage entity alignment: combining hybrid knowledge graph embedding with similarity-based relation alignment. In: The 16th Pacific Rim International Conference on Artificial Intelligence, pp. 162–175 (2019)
Li, J., Bu, C., Li, P., Wu, X.: A coarse-to-fine collective entity linking method for heterogeneous information networks. Knowl.-Based Syst. 288(2), 107286 (2021)
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)
Pujara, J., Augustine, E., Getoor, L.: Sparsity and noise: where knowledge graph embeddings fall short. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1751–1756 (2017)
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)
Zhang, Z., Cai, J., Zhang, Y., Wang, J: Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3065–3072 (2020)
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. 30th AAAI Conf. Artif. Intell. 30(1) (2016)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web 8(3), 489–508 (2017)
Melo, A., Paulheim, H.: Detection of relation assertion errors in knowledge graphs. In: Proceedings of the Knowledge Capture Conference, pp. 22:1–22:8 (2017)
De Meo, P., Ferrara, E., Fiumara, G., Ricciardello, A.: A novel measure of edge centrality in social networks. Knowl.-Based Syst. 30, 136–150 (2012)
Xie, R., Liu, Z., Lin, F., Lin, L.: Does William Shakespeare really write Hamlet? Knowledge representation learning with confidence. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 4954–4961 (2018)
Jia, S., Xiang, Y., Chen, X., Wang, K.: Triple trustworthiness measurement for knowledge graph. In: The World Wide Web Conference, pp. 2865–2871 (2019)
Shan, Y., Bu, C., Liu, X., Ji, S., Li, L.: Confidence-aware negative sampling method for noisy knowledge graph embedding. In: 2018 IEEE International Conference on Big Knowledge, pp. 33–40 (2018)
Kimmig, A., Bach, S., Broecheler, M., Huang, B., Getoor, L: A short introduction to probabilistic soft logic. In: NIPS Workshop on PPFA, pp.1–4 (2012)
Hong, Y., Bu, C., Jiang, T.: Rule-enhanced noisy knowledge graph embedding via low-quality error detection. In: IEEE International Conference on Knowledge Graph, pp. 544–551 (2020)
Bu, C., Yu, X, Hong, Y., Jiang, T.: Low-quality error detection for noisy knowledge graph. J. Database Manage. 32(4), article 4
Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE. VLDB J. 24(6), 707–730 (2015)
Acknowledgments
This work was partly supported by the National Natural Science Foundation of China (No. 61806065 and No. 91746209), the Fundamental Research Funds for the Central Universities (No. JZ2020HGQA0186), and the Project funded by the China Postdoctoral Science Foundation (No. 2018M630704).
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Hong, Y., Bu, C., Wu, X. (2021). High-Quality Noise Detection for Knowledge Graph Embedding with Rule-Based Triple Confidence. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13031. Springer, Cham. https://doi.org/10.1007/978-3-030-89188-6_43
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