ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (8): 1758-1771.doi: 10.7544/issn1000-1239.2019.20190169

Special Issue: 2019人工智能前沿进展专题

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Low-Redundancy Knowledge Graph Management with Range Query Support

Wang Fei, Qian Tieyun, Liu Bin, Peng Zhiyong   

  1. (School of Computer Science, Wuhan University, Wuhan 430072)
  • Online:2019-08-01

Abstract: As more and more data is published in the form of knowledge graph, the management of which attracts a lot of attention. Existing approaches for knowledge graph management have two drawbacks: 1) logical storage modeling generates lots of redundancy and ineffectively supports range queries on continuous attributes; 2) semantic storage modeling costs much and inefficiently adapts to the dynamic evolution of knowledge graph. In this paper, we propose a novel method called cluster object deputy model (CODM) to manage knowledge and metadata. The model has two key properties, namely logical storage modeling of schema and semantic storage modeling of lightweight. To this end, we design a schema cluster algorithm based on the set editing distance to convert knowledge graph into schema data, which realizes schema storage of data and supports index specification of attribute type. Besides, CODM constructs a class hierarchical system to model different associations among entities. It adopts object pointers to achieve the lightweight materialization of generalized semantic association. Experimental results show that CODM can tremendously reduce the data redundancy and outperforms the state-of-the-art methods in terms of range queries. And those results also indicate that CODM can accelerate the processing of complex queries.

Key words: knowledge graph, metadata modeling, range query, schema storage, cluster object deputy model (CODM)

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