Knowledge Graph Reasoning Based on Paths of Tensor Factorization
WU Yunbing1, ZHU Danhong1, LIAO Xiangwen1,2, ZHANG Dong1, LIN Kaibiao3
1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116 2. Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou University, Fuzhou 350116 3. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024
Abstract In the existing tensor factorization techniques used in knowledge graph learning and reasoning, only direct links between entities are taken into account. However, the graph structure of knowledge graph is ignored. In this paper, knowledge graph reasoning based on paths of tensor factorization is proposed. The path ranking algorithm(PRA) is employed to find all paths connecting the source and target nodes in a relation instances. Then, those paths are decomposed by tensor factorization. And the entities and relations are optimized by the alternating least squares method. Experimental results on two large-scale knowledge graphs show the algorithm achieves significant and consistent improvement on tasks of entities linking prediction and paths question answering and its prediction accuracy outperforms that of other related models.
About author:: (WU Yunbing, born in 1976, master, lecturer. His research interests include data mining, machine learning and knowledge re-presentation.) (ZHU Danhong, born in 1981, master, lecturer. Her research interests include image processing, image retrieval and data mining.) (LIAO Xiangwen(Corresponding author), born in 1980, Ph.D., associate professor. His research interests include text mining and information retrieval.) (ZHANG Dong, born in 1981, Ph.D., associate professor. His research interests include computer networks and network virtua-lization.) >(LIN Kaibiao, born in 1980, Ph.D., lecturer. His research interests include data mining and artificial intelligence.)
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2017, Vol. 30 
