A Knowledge Base Completion Model Based on Path Feature Learning

IAENG Internaitonal journal of computer science

Xu Wang¹  Xixun Lin¹  Yanchun Liang¹  Renchu Guan¹  Limin Wang²  Mary Qu Yang³ 

[1] Jilin University;Jilin University of Finance and Economics;university of arkansas at little rock

关键词: knowledge base completion;    random walks;    path features;    extreme learning machine;   

DOI  :  10.15837/ijccc.2018.1.3104

学科分类：计算机科学（综合）

来源: International Association of Engineers

【 摘 要 】

Large-scale knowledge bases, as the foundations for promoting the development of artificial intelligence, have attracted increasing attention in recent years. These knowledge bases contain billions of facts in triple format; yet, they suffer from sparse relations between entities. Researchers proposed the path ranking algorithm (PRA) to solve this fatal problem. To improve the scalability of knowledge inference, PRA exploits random walks to find Horn clauses with chain structures to predict new relations given existing facts. This method can be regarded as a statistical classification issue for statistical relational learning (SRL). However, large-scale knowledge base completion demands superior accuracy and scalability. In this paper, we propose the path feature learning model (PFLM) to achieve this urgent task. More precisely, we define a two-stage model: the first stage aims to learn path features from the existing knowledge base and extra parsed corpus; the second stage uses these path features to predict new relations. The experimental results demonstrate that the PFLM can learn meaningful features and can achieve significant and consistent improvements compared with previous work.

【 授权许可】

CC BY