期刊
NEURAL COMPUTING & APPLICATIONS
卷 31, 期 9, 页码 5629-5639出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-018-3384-6
关键词
Knowledge graph completion; Relation paths; Path projection; Type constraints; Knowledge representation learning
资金
- National Natural Science Foundation of China [61572228, 61472158, 61300147, 61602207]
- Science Technology Development Project from Jilin Province [20160101247JC]
- Zhuhai Premier Discipline Enhancement Scheme
- Guangdong Premier Key-Discipline Enhancement Scheme
- European Commission [600854]
Large-scale knowledge graphs have currently reached impressive sizes; however, they are still far from complete. In addition, most existing methods for knowledge graph completion only consider the direct links between entities, ignoring the vital impact of the semantics of relation paths. In this paper, we study the problem of how to better embed entities and relations of knowledge graphs into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths and propose a novel relation path embedding model named as RPE. Specifically, with the corresponding relation and path projections, RPE can simultaneously embed each entity into two types of latent spaces. Moreover, type constraints are extended from traditional relation-specific type constraints to the proposed path-specific type constraints and both of the two type constraints can be seamlessly incorporated into RPE. The proposed model is evaluated on the benchmark tasks of link prediction and triple classification. The results of experiments demonstrate our method outperforms all baselines on both tasks. They indicate that our model is capable of catching the semantics of relation paths, which is significant for knowledge representation learning.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据