Affiliation:
1. Information Science and Technology College, Dalian Maritime University, China
Abstract
Knowledge graphs exhibit a typical hierarchical structure and find extensive applications in various artificial intelligence domains. However, large-scale knowledge graphs need to be completed, which limits the performance of knowledge graphs in downstream tasks. Knowledge graph embedding methods have emerged as a primary solution to enhance knowledge graph completeness. These methods aim to represent entities and relations as low-dimensional vectors, focusing on handling relation patterns and multi-relation types. Researchers need to pay more attention to the crucial feature of hierarchical relationships in real-world knowledge graphs. We propose a novel knowledge graph embedding model called Hierarchy-Aware Paired Relation Vectors Knowledge Graph Embedding (HPRE) to bridge this gap. By leveraging the power of 2D coordinates, HPRE adeptly model relation patterns, multi-relation types, and hierarchical features in the knowledge graph. Specifically, HPRE employs paired relation vectors to capture the distinct characteristics of head and tail entities, facilitating a better fit for relational patterns and multi-relation scenarios. Additionally, HPRE employs angular coordinates to differentiate entities at various levels of the hierarchy, effectively representing the hierarchical nature of the knowledge graph. The experimental results show that the HPRE model can effectively learn the hierarchical features of the knowledge graph and achieve state-of-the-art experimental results on multiple real-world datasets for the link prediction task.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference45 articles.
1. Mintz M. , Bills S. , Snow R. and Jurafsky D. , Distant supervision for relation extraction without labeled data, In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pages 1003–1011 (2009).
2. Building watson: An overview of the deepqa project;Ferrucci;AImagazine,2010
3. Reinforced negative sampling over knowledge graph for recommendation;Wang;Proceedings of the web conference 2020,2020
4. Bordes A. , Usunier N. , Garcia-Duran A. , Weston J. and Yakhnenko O. , Translating embeddings for modeling multi-relational data, Advances in Neural Information Processing Systems 26 (2013).
5. Sun Z. , Deng Z.-H. , Nie J.-Y. and Tang J. , Rotate: Knowledge graphembedding by relational rotation in complex space, arXiv preprint arXiv:1902.10197, 2019.