[1]RAO Guanjun,GU Tianlong,CHANG Liang,et al.Knowledge graph embedding based on similarity negative sampling[J].CAAI Transactions on Intelligent Systems,2020,15(2):218-226.[doi:10.11992/tis.201811022]
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Knowledge graph embedding based on similarity negative sampling
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 2
Page number:
218-226
Column:
学术论文—知识工程
Public date:
2020-03-05
- Title:
- Knowledge graph embedding based on similarity negative sampling
- Keywords:
- knowledge graph; representation learning; random sampling; similarity sampling; K-means clustering; stochastic gradient descent; link prediction; triple classification
- CLC:
- TP391
- DOI:
- 10.11992/tis.201811022
- Abstract:
- For the existing knowledge graph embedding model, the random extraction of an entity from the entity set results in the generation of lower-quality negative triples, and this affects the feature learning ability of the entity and the relationship. In this paper, we study the related factors affecting the quality of negative triples, and propose an entity similarity negative sampling method to generate high-quality negative triples. In the similarity negative sampling method, all entities are first divided into a number of groups using the K-means clustering algorithm. Then, corresponding to each positive triple, an entity is selected to replace the head entity from the cluster, whereby the head entity is located in the positive triple, and the tail entity is replaced in a similar approach. TransE-SNS is obtained by combining the similarity negative sampling method with TransE. Experimental results show that TransE-SNS has made significant progress in link prediction and triplet classification tasks.
- References:
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