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A neural translating general hyperplane for knowledge graph embedding
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DOI：10.1016/j.jocs.2018.11.004.png)
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Abstract
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For completing knowledge graph, many translation-based models, like that Trans(E and H) which embed a knowledge graph into a continuous vector space and encode relations as translation operations in that space, have achieved better performance. However, most of them have limitations in expressing complex relations for knowledge graph. In this paper, we propose a translation-neural based method NTransGH for knowledge graph completion. NTransGH combines translation mechanism for modeling relations as translation operations by generalized hyperplanes, and a neural network for capturing more complex interactions between entities and relations. We conduct experiment on two tasks link prediction and triplet classification with two datasets. Experimental results show that NTransGH has strong expression in mapping properties of complex relations, and achieves significant and consistent improvements over state-of-the-art embedding methods. This paper is an extension of our previous works [1]. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Knowledge representation
Knowledge embedding
Knowledge graph completion
Link prediction
Neural network
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