Recalibration convolutional networks for learning interaction knowledge graph embedding

计算机科学 嵌入 理论计算机科学 一般化 人工智能 特征学习 图形 知识图 代表(政治) 关系(数据库) 多义 语义学(计算机科学) 机器学习 数据挖掘 数学 政治 数学分析 程序设计语言 法学 政治学
作者
Zhifei Li,Hai Liu,Zhaoli Zhang,Tingting Liu,Jiangbo Shu
出处
期刊:Neurocomputing [Elsevier BV]
卷期号:427: 118-130 被引量:37
标识
DOI:10.1016/j.neucom.2020.07.137
摘要

Knowledge graph embedding aims to learn the embedded representation of entities and relations in knowledge graphs which is very important for the subsequent link prediction task. However, two key issues are existed for learning knowledge graph embedding: 1) How to take full advantage of the deep learning algorithms to generate expressive embeddings? 2) How to solve the polysemy phenomenon caused by multi-relations knowledge graphs that entities and relations show different semantics after involving different predictions? In this article, to tackle the first problem, the multi-layer convolutional networks are adopted to generate features about entities and relations then used to predict candidate entity. Moreover, the representation power of the networks is strengthened by integrating an effective recalibration mechanism which can accentuate informative features selectively. To tackle the second problem, we propose to learn multiple specific interaction embeddings. Instead of directly learning one general embedding to preserve all information for each entity and relation, their interactions are captured to model the cross-semantic influence from relations to entities and from entities to relations. Compared to traditional embedding models, the proposed model can provide more generalization capabilities and effectively capture potential links between entities and relations. Experimental results have revealed that the proposed model achieves the state-of-the-art performance for general evaluation metrics on link prediction tasks.
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