A Knowledge Base Completion Model Based on Path Feature Learning

  • Xixun Lin Jilin University
  • Yanchun Liang Jilin University
  • Limin Wang Jilin University of Finance and Economics
  • Xu Wang Jilin University
  • Mary Qu Yang university of arkansas at little rock
  • Renchu Guan Jilin University


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.


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How to Cite
LIN, Xixun et al. A Knowledge Base Completion Model Based on Path Feature Learning. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 1, p. 71-82, feb. 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3104>. Date accessed: 06 mar. 2021. doi: https://doi.org/10.15837/ijccc.2018.1.3104.


knowledge base completion, random walks, path features, extreme learning machine