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However, it ignores the characteristics of the graph structure of knowledge graph. To solve this problem, a knowledge graph reasoning algorithm based on multihop relational paths learning (MHRP-learning) and tensor decomposition is proposed in this paper. Firstly, MHRP-learning is adopted to obtain the relationship path between entity pairs in the knowledge graph. Then, the tensor decomposition is performed to get a novel learning framework. Finally, experiments show that the proposed method achieves advanced results, and it is applicable to knowledge graph reasoning.<\/jats:p>","DOI":"10.1155\/2021\/8880553","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T16:35:09Z","timestamp":1616171709000},"page":"1-6","source":"Crossref","is-referenced-by-count":4,"title":["Knowledge Graph Reasoning Based on Tensor Decomposition and MHRP-Learning"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4426-2622","authenticated-orcid":true,"given":"Tangsen","family":"Huang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China"}]},{"given":"Xiaowu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China"}]},{"given":"Sheping","family":"Zhai","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi 710121, China"}]},{"given":"Juanli","family":"Wei","sequence":"additional","affiliation":[{"name":"Network Information Center, Xi\u2019an Physical Education University, Xi\u2019an, Shaanxi 710068, China"}]}],"member":"311","reference":[{"issue":"2","key":"1","first-page":"135","article-title":"Principles of semantic networks: exploration in the representation of knowledge","author":"J. 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