Abstract：Knowledge graph data has large volumes, rich content, diverse types, and lacks a unified model description. Pattern information needs to be extracted from knowledge graphs to improve the quality of knowledge graph retrieval and mining. This paper presents a knowledge graph summarization pattern and quality metrics. This method is used in an algorithm for mining Top-k summarization patterns (Top-k SPM) formulated as a submodular function optimization problem. Then, a Pregel based parallel algorithm is used to validate the algorithm and measure the qualities of summarization patterns. Two efficient greedy algorithms are also presented to solve the Top-k SPM. The efficiency and effectiveness of the method is then verified on real knowledge graph datasets. The tests show that the method outperforms the existing methods in terms of coverage and algorithm execution time.
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2019, Vol. 59 
