[1]WANG Kun,XIE Zhenping,CHEN Meijie.Modeling knowledge network on associative relations based on graph reduction[J].CAAI Transactions on Intelligent Systems,2019,14(4):679-688.[doi:10.11992/tis.201808009]
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Modeling knowledge network on associative relations based on graph reduction
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 4
Page number:
679-688
Column:
学术论文—知识工程
Public date:
2019-07-02
- Title:
- Modeling knowledge network on associative relations based on graph reduction
- Keywords:
- knowledge graph; associative memory; knowledge modeling; graph reduction; knowledge network; knowledge association; memory reservation; relational network
- CLC:
- TP391
- DOI:
- 10.11992/tis.201808009
- Abstract:
- Inspired by the fact that knowledge is stored in the form of associative memory in the human brain, we discuss the internal associative network model of knowledge system using associative relations, in order to provide a new train of thoughts referential to modeling knowledge graph. For a given knowledge corpus, an initial knowledge relation network was first constructed by producing direct associative relations, and then several graph reduction methods were introduced to optimize the modeling efficiency. Random selection and local and global strong memory reservation strategies were designed to reform the associative relations and their associative intensities, and experimental datasets were used to analyze these three modeling strategies. The experimental results show that the three different strategies exhibit interesting common characteristics. Moreover, global strong memory reservation strategy can optimize balance between the size of knowledge associative relation network and the associative memory efficiency. The results can provide a basis for related applications, as well as provide a meaningful understanding for exploring human-like knowledge associative memory modeling problems.
- References:
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Last Update:
2019-08-25