CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Knowledge Graph Embedding via Dynamic Mapping Matrix
Ji Guoliang; He Shizhu; Xu Liheng; Liu Kang; Zhao Jun
2015-07
会议名称Annual Meeting of the Association for Computational Linguistics
会议日期2015年7月26日至31日
会议地点中国北京
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.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14515
专题模式识别国家重点实验室_自然语言处理
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],2015.
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