摘要： 语义图概要的目的是提取语义图的关键信息，形成原数据集的概要模型以解决大规模语义图的理解、查询、应用难题。为提升现有语义图概要方法效率，提出一种基于本体分割的概要方法。通过本体分割算法对语义图进行分割生成扩展子图。采用形式概念分析对每个扩展子图生成元素的偏序格（又称特征集格）。在此基础上，由所有子图的特征集格形成了原语义图的概要。在关联开放数据集和Berlin SPARQL Benchmark数据集上的实验结果表明，该方法具有较好的可扩展性，有效提高了概要方法的效率。

关键词: 语义图, 知识图谱, 关联开放数据, 语义图概要, 形式概念分析

Abstract: Semantic graph summarization is to extract key information from semantic graphs, and generate a summarized model of the original data set to solve problems in understanding, querying, and using large-scale semantic graphs.In order to improve the efficiency of current summarization algorithms, this paper proposes an approach of generating summaries based on ontology partition.The ontology partition algorithm is used to divide the semantic graph into sub-graphs.Then for each sub-graph, its partially ordered lattices (also named characteristic set lattices) of elements are generated using formal concept analysis.On this basis, the characteristic set lattices of all sub-graphs form the summarization of the original semantic graphs.The approach is tested on the Linked Open Data(LOD) dataset and the Berlin SPARQL Benchmark datasets.Results show that the proposed approach exhibits excellent scalability, and a significantimprovement in summarization efficiency.

Key words: semantic graph, knowledge graph, Linked Open Data(LOD), Semantic Graph Summarization(SGS), Formal Concept Analysis(FCA)

中图分类号: 

• TP391