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

Publisher:
AAAI
Publication Type:
Conference Proceeding
Citation:
Proceedings of the National Conference on Artificial Intelligence, 2015
Issue Date:
2015-01-01
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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 long-range interactions' (referred to as SePLi). Comparing with the-state-of-the-art models, SePLi achieves better performances of embeddings.
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