CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Knowledge Graph Completion with Adaptive Sparse Transfer Matrix
Ji Guoliang; Liu Kang; He Shizhu; Zhao Jun
2016-02
Conference NameAAAI Conference on Artificial Intelligence
Conference Date2016年2月12日至17日
Conference PlacePhoenix, Arizona USA
AbstractWe model knowledge graphs for their completion by encoding each entity and relation into a numerical space. All previous work including Trans(E, H, R, and D) ignore the heterogeneity (some relations link many entity pairs and others do not) and the imbalance (the number of head entities and that of tail entities in a relation could be different) of knowledge graphs. In this paper, we propose a novel approach TranSparse to deal with the two issues. In TranSparse, transfer matrices are replaced by adaptive sparse matrices, whose sparse degrees are determined by the number of entities (or entity pairs) linked by relations. In experiments, we design structured and unstructured sparse patterns for transfer matrices and analyze their advantages and disadvantages. We evaluate our approach on triplet classification and link prediction tasks. Experimental results show that TranSparse outperforms Trans(E, H, R, and D) significantly, and achieves state-of-the-art performance.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14516
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorLiu Kang
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ji Guoliang,Liu Kang,He Shizhu,et al. Knowledge Graph Completion with Adaptive Sparse Transfer Matrix[C],2016.
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