Knowledge Graph Embedding via Dynamic Mapping Matrix

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	Knowledge Graph Embedding via Dynamic Mapping Matrix
	Ji Guoliang; He Shizhu; Xu Liheng; Liu Kang; Zhao Jun
	2015-07
会议日期	2015年7月26日至31日
会议地点	中国北京
英文摘要	Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as TransE, TransH and TransR/CTransR regard a relation as translation from head entity to tail entity and the CTransR achieves state-of-the-art performance. In this paper, we propose a more fine-grained model named TransD, which is an improvement of TransR/CTransR. In TransD, we use two vectors to represent a named symbol object (entity and relation). The first one represents the meaning of a(n) entity (relation), the other one is used to construct mapping matrix dynamically. Compared with TransR/CTransR, TransD not only considers the diversity of relations, but also entities. TransD has less parameters and has no matrix-vector multiplication operations, which makes it can be applied on large scale graphs. In Experiments, we evaluate our model on two typical tasks including triplets classification and link prediction. Evaluation results show that our approach outperforms state-of-the-art methods.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/14515]
专题	自动化研究所_模式识别国家重点实验室_自然语言处理团队
通讯作者	Liu Kang
作者单位	Institute of Automation Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Ji Guoliang,He Shizhu,Xu Liheng,et al. Knowledge Graph Embedding via Dynamic Mapping Matrix[C]. 见:. 中国北京. 2015年7月26日至31日.