Knowledge Base Completion via Rule-Enhanced Relational Learning

被引：7

作者：

Guo, Shu ^{[1
,2
]}

Ding, Boyang ^{[1
,2
]}

Wang, Quan ^{[1
,2
]}

Wang, Lihong ^{[3
]}

Wang, Bin ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China

[2] Univ Chinese Acad Sci, Beijing, Peoples R China

[3] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing, Peoples R China

来源：

KNOWLEDGE GRAPH AND SEMANTIC COMPUTING: SEMANTIC, KNOWLEDGE, AND LINKED BIG DATA | 2016年 / 650卷

基金：

中国国家自然科学基金;

关键词：

Knowledge base completion; Relational learning; Rules;

D O I：

10.1007/978-981-10-3168-7_22

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Traditional relational learning techniques perform the knowledge base (KB) completion task based solely on observed facts, ignoring rich domain knowledge that could be extremely useful for inference. In this paper, we encode domain knowledge as simple rules, and propose rule-enhanced relational learning for KB completion. The key idea is to use rules to further refine the inference results given by traditional relational learning techniques, and hence improve the inference accuracy of them. Facts inferred in this way will be the most preferred by relational learning, and at the same time comply with all the rules. Experimental results show that by incorporating the domain knowledge, our approach achieve the best overall performance in the CCKS 2016 competition.

引用

页码：219 / 227

页数：9

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