Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 1
39-43

Improved adaptive embedding method for knowledge graph representation

Meng Xiaoyan1,2,3,4
Jiang Tonghai1,3
Zhou Xi1,3
Han Yunfei1,3
Ma Bo1,3
1. The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Xinjiang Laboratory of Minority Speech & Language Information Processing, Chinese Academy of Sciences, Urumqi 830011, China
4. College of Computer & Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
DOI: 10.19734/j.issn.1001-3695.2019.11.0605

Abstract

TransE, the embedding representation method, is the most classic translation-based method. But it also has two defects. One is the limitation in dealing with complex relations. The other one is Euclidean distance is used as a measure in the scoring function and each feature dimension is calculated with the same weight, so the accuracy will be affected by irrelevant dimensions and the flexibility is lower. Therefore, in view of these two defects, this paper proposed an adaptive KG embedding representation method, namely, TransAD. It replaced the measure function and then introduced a diagonal weight matrix into the score function to assign weights to each feature dimension respectively to increase the representation ability of the model. At the same time, inspired by TransD, it built a spatial projection model of entity and established relationship through dynamic mapping matrix to enhance the processing ability of the model for complex relations. Finally, it integrated the two optimizations into the TransAD model. The experimental results show that TransAD is superior to Trans(E, H, R, D) , and it is advanced in various indexes of link prediction and triad classification tasks and has certain advantages.

Foundation Support

中国科学院STS计划项目(KFJ-STS-QYZD-102)
中国科学院青年创新促进会项目(Y9290802)
中科院西部之光—西部青年学者A类资助项目(2018-XBQNXZ-A-003)
自治区天山青年计划项目(2018Q032)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.11.0605
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 1
Section: Algorithm Research & Explore
Pages: 39-43
Serial Number: 1001-3695(2021)01-007-0039-05

Publish History

[2021-01-05] Printed Article

Cite This Article

孟小艳, 蒋同海, 周喜, 等. 一种改进的自适应知识图谱嵌入式表示方法 [J]. 计算机应用研究, 2021, 38 (1): 39-43. (Meng Xiaoyan, Jiang Tonghai, Zhou Xi, et al. Improved adaptive embedding method for knowledge graph representation [J]. Application Research of Computers, 2021, 38 (1): 39-43. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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