Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (5): 36-48.doi: 10.16088/j.issn.1001-6600.2022031004

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Knowledge Matrix Representation

ZHANG Shichao1*, LI Jiaye2   

  1. 1. School of Computer Science and Engineering, Guangxi Normal University, Guilin Guangxi 541004, China;
    2. School of Computer Science and Engineering, Central South University, Changsha Hunan 410083, China
  • Received:2022-03-10 Revised:2022-05-15 Online:2022-09-25 Published:2022-10-18

Abstract: Knowledge representation is the basis of all artificial intelligence algorithms. It can represent human knowledge as a data structure that can be processed by machines. This paper shows the knowledge matrix representation, which can fundamentally simplify the operation process of the algorithm and improve the operation efficiency of the algorithm. Specifically, this paper first transforms 13 kinds of interval relationship representations in temporal reasoning into 5×5 matrix. On this basis, the calculation and propagation of temporal relations can be obtained through matrix calculation. Then, the relationship between production rules is transformed into a matrix and encoded into a calculation formula, which is a mathematical model of uncertain reasoning. Finally, the sample relationship is expressed as a matrix, and K values and K nearest neighbors can be obtained in one step calculation. It can model the lazy learning part of KNN classification, i.e., transform it into a mathematical model. In addition, this paper also looks forward to the future work of knowledge matrix representation, and points out that knowledge matrix representation needs to define different calculation methods for different applications.

Key words: knowledge representation, matrix representation, relation calculus, temporal reasoning, KNN classification

CLC Number: 

  • TP181
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