Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base
By Fei Wu, Jun Song, Yi Yang, Xi Li, Zhongfei Zhang, Yueting Zhuang. 2015
14 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Kevin Chen-Chuan Chang
- Vincent Wenchen Zheng
- Hongyun Cai
- Cheng Guo
- Felix Berkhahn
- Evgeny Burnaev
- Sergey Ivanov
Citations in the corpus
- A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “In proximity search [39], [44], they rank the nodes of a particular type (e.”
- Entity Embeddings of Categorical Variables (2016, background): “More recently, knowledge base which is a large collection of complex relational data is seeing lots of works using entity embedding[22–24].”