TransHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction

• URL: http://arxiv.org/abs/2204.13221v1
• Date: Wed, 27 Apr 2022 22:49:27 GMT
• Title: TransHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction
• Authors: Yizhi Li, Wei Fan, Chao Liu, Chenghua Lin, Jiang Qian
• Abstract summary: We propose a novel score function TransHER for knowledge graph embedding. Our model first maps entities onto two separate hyper-ellipsoids and then conducts a relation-specific translation on one of them. Experimental results show that TransHER can achieve state-of-the-art performance and generalize to datasets in different domains and scales.
• Score: 14.636054717485207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graph embedding methods are important for knowledge graph completion (link prediction) due to their robust performance and efficiency on large-magnitude datasets. One state-of-the-art method, PairRE, leverages two separate vectors for relations to model complex relations (i.e., 1-to-N, N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly restricts entities on the hyper-ellipsoid surface and thus limits the optimization of entity distribution, which largely hinders the performance of knowledge graph completion. To address this problem, we propose a novel score function TransHER, which leverages relation-specific translations between head and tail entities restricted on separate hyper-ellipsoids. Specifically, given a triplet, our model first maps entities onto two separate hyper-ellipsoids and then conducts a relation-specific translation on one of them. The relation-specific translation provides TransHER with more direct guidance in optimization and the ability to learn semantic characteristics of entities with complex relations. Experimental results show that TransHER can achieve state-of-the-art performance and generalize to datasets in different domains and scales. All our code will be publicly available.

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