Abstract
Knowledge graph (KG) completion aims at predicting the unknown links between entities and relations. In this paper, we focus on this task through embedding a KG into a latent space. Existing embedding based approaches such as TransH usually perform the same operation on head and tail entities in a triple. Such way could ignore the different roles of head and tail entities in a relation. To resolve this problem, this paper proposes a novel method for KGs embedding by preforming bi-directional projections on head and tail entities. In this way, the different information of an entity could be elaborately captured when it plays different roles for a relation. The experimental results on multiple benchmark datasets demonstrate that our method significantly outperforms state-of-the-art methods.
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Notes
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We use the fact that the results of the formula \( {\text{w}}^{T} {\text{hw}} \) equal with the formula \( {\text{ww}}^{T} {\text{h}} \).
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Nos. 61462043, 61272212, and 61562042), and the Science and Technology Foundation of Jiangxi Province (No. 20151BAB217014).
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Luo, W., Zuo, J., Gao, Z. (2016). Knowledge Graph Completion by Embedding with Bi-directional Projections. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_71
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