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Path-specific knowledge graph embedding
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DOI：10.1016/j.knosys.2018.03.020.png)
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Abstract
En 中文
Knowledge graph embedding aims to represent entities, relations and multi-step relation paths of a knowledge graph as vectors in low-dimensional vector spaces, and supports many applications, such as entity prediction, relation prediction, etc. Existing embedding methods learn the representations of entities, relations, and multi-step relation paths by minimizing a general margin-based loss function shared by all relation paths. This setting fails to consider the differences among different relation paths. In this paper, we propose an embedding method by minimizing a path-specific margin-based loss function for knowledge graph embedding, called PaSKoGE. For each path, it adaptively determines its margin-based loss function by encoding the correlation between relations and multi-step relation paths for any given pair of entities. PaSKoGE outperforms the-state-of-the-art methods. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Path-specific
Knowledge graph embedding
Relation path
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