Knowledge Graph Embedding by Flexible Translation

Knowledge graph embedding refers to projecting entities and relations in knowledge graph into continuous vector spaces. Current state-of-the-art models are translation-based model, which build embeddings by treating relation as translation from head entity to tail entity. However, previous models is too strict to model the complex and diverse entities and relations(e. g. symmetric/transitive/one-to-many/many-to-many relations). To address these issues, we propose a new principle to allow flexible translation between entity and relation vectors. We can design a novel score function to favor flexible translation for each translation-based models without increasing model complexity. To evaluate the proposed principle, we incorporate it into previous method and conduct triple classification on benchmark datasets. Experimental results show that the principle can remarkably improve the performance compared with several state-of-the-art baselines. (a) TransE (b) Flexible Translation Figure 1: Illustration of TransE and our proposed Flexible Translation. There are three triples, which share the same head entity (“Michael Jackson”) and the same relation (“publish song”), while having three different tail entities (“Beat it”, “Billie Jean”, and “Thriller”). (a) TransE can hardly distinguish different tail entities as they all approximated to the sum of head vector and relation vector. (b) Instead of strictly constraining h+r=t, our principle is to enforce that h+r has the same direction with t.

Authors

• Jun Feng Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology
• Minlie Huang Tsinghua University, Tsinghua University The CoAI Group, DCST, Tsinghua University
• Mingdong Wang
• Mantong Zhou
• Yu Hao
• Xiaoyan Zhu Xi'an Jiaotong University

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