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Open AccessArticle

ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion

School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
National Digital Switching System Engineering & Technological R&D Center, Zhengzhou 450002, China
State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, China
School of Computer Science, Shenzhen Institute of Information Technology, Shenzhen 518172, China
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
School of Computer Science and Engineering, VIT University, Vellore 632014, India
Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China
Author to whom correspondence should be addressed.
Symmetry 2019, 11(9), 1096;
Received: 31 July 2019 / Revised: 25 August 2019 / Accepted: 27 August 2019 / Published: 2 September 2019
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data 2019)
Link prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge graph. Previous research on link predication only focuses on path or semantic-based features, which can hardly have a full insight of features between entities and may result in a certain ratio of false inference results. To improve the accuracy of link predication, we propose a novel approach named Entity Link Prediction for Knowledge Graph (ELPKG), which can achieve a high accuracy on large-scale knowledge graphs while keeping desirable efficiency. ELPKG first combines path and semantic-based features together to represent the relationships between entities. Then it adopts a probabilistic soft logic-based reasoning method that effectively solves the problem of non-deterministic knowledge reasoning. Finally, the relation between entities is completed based on the entity link prediction algorithm. Extensive experiments on real dataset show that ELPKG outperforms baseline methods on [email protected], [email protected], and MRR. View Full-Text
Keywords: relation completion; knowledge graph completion; link prediction; probabilistic soft logic relation completion; knowledge graph completion; link prediction; probabilistic soft logic
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Ma, J.; Qiao, Y.; Hu, G.; Wang, Y.; Zhang, C.; Huang, Y.; Sangaiah, A.K.; Wu, H.; Zhang, H.; Ren, K. ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion. Symmetry 2019, 11, 1096.

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