[Objective] This paper proposes a knowledge graph model based on negative sampling and joint relational contexts, aiming to improve the quality of current translation-based knowledge graph embedding models. [Methods] Firstly, we extracted the neighbors of the target instances from the original knowledge graph to generate the context vector. Then, we decided the properties of adjacent relations, which also provided information on the nature or type of a given entity. Third, we used the Concat function to aggregate contexts of the given entities of negative sampling and determined the entity attributes to be replaced. Finally, we adopted the triple embedding of the TransE model to generate negative triples, and improved the similarities of positive and negative triples. [Results] We examined the proposed model with data sets of FB15K-237 and WN18RR. The entity link was 18.3% and 29.2% higher than those of the benchmark model. Meantime, the relationship link was 0.7% better than the optimal result of the benchmark model. [Limitations] Our model only included the semantics of the relational contexts, which is very hard to determine their relative positions. [Conclusions] The proposed sampling strategy effectively improves the quality of negative triples, as well as the accuracy of knowledge graph.
李智杰, 王瑞, 李昌华, 张颉. 联合关系上下文负采样的知识图谱嵌入*[J]. 数据分析与知识发现, 2022, 6(12): 90-98.
Li Zhijie, Wang Rui, Li Changhua, Zhang Jie. Embedding Knowledge Graph with Negative Sampling and Joint Relational Contexts. Data Analysis and Knowledge Discovery, 2022, 6(12): 90-98.
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