Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base

We consider the problem of embedding entities and relations of knowledge bases into low-dimensional continuous vector spaces (distributed representations). Unlike most existing approaches, which are primarily efficient for modelling pairwise relations between entities, we attempt to explicitly model both pairwise relations and long-range interactions between entities, by interpreting them as linear operators on the low-dimensional embeddings of the entities. Therefore, in this paper we introduces path ranking to capture the long-range interactions of knowledge graph and at the same time preserve the pairwise relations of knowledge graph; we call it structured embedding via pairwise relation and longrange interactions (referred to as SePLi). Comparing with the-state-of-the-art models, SePLi achieves better performances of embeddings.

Authors

• Fei Wu Zhejiang University
• Jun Song Hong Kong Baptist University
• Yi Yang
• Xi Li College of Computer Science and Technology, Zhejiang University
• Zhongfei Zhang Binghamton University, SUNY
• Yueting Zhuang Zhejiang University

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