ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (12): 2549-2561.doi: 10.7544/issn1000-1239.2019.20190648

Special Issue: 2019大数据知识工程及应用专题

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Open Knowledge Graph Representation Learning Based on Neighbors and Semantic Affinity

Du Zhijuan1, Du Zhirong2, Wang Lu3   

  1. 1(College of Computer Science, Inner Mongolia University, Hohhot 010021);2(College of Information, North China University of Technology, Beijing 100144);3(Institute of Scientific and Technical Information of China, Beijing 100038)
  • Online:2019-12-01

Abstract: Knowledge graph (KG) breaks the data isolation in different scenarios and provides basic support for the practical application. The representation learning transforms KG into the low-dimensional vector space to facilitate KG application. However, there are two problems in KG representation learning: 1)It is assumed that KG satisfies the closed-world assumption. It requires all entities to be visible during the training. In reality, most KGs are growing rapidly, e.g. a rate of 200 new entities per day in the DBPedia. 2)Complex semantic interaction, such as matrix projection and convolution, are used to improve the accuracy of the model and limit the scalability of the model. To this end, we propose a representation learning method TransNS for open KG that allows new entities to exist. It selects the related neighbors as the attribute of the entity to infer the new entity, and uses the semantic affinity between the entities to select the negative triple in the learning phase to enhance the semantic interaction capability. We compare our TransNS with the state-of-the-art baselines on 5 traditional and 8 new datasets. The results show that our TransNS performs well in the open KGs and even outperforms existing models on the baseline closed KGs.

Key words: knowledge graph, representation learning, open-world assumption, neighbors, semantic affinity

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