基于K-Shell分解算法的过程知识图谱I-PageRank算法模型研究

Q3 Arts and Humanities
Icon Pub Date : 2023-03-01 DOI:10.1109/ICNLP58431.2023.00082
Yanwei Huo, Hongyu Cheng
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引用次数: 0

摘要

PageRank算法在计算节点时是均匀分布到节点链的所有节点上,但在实际生产制造企业中,工艺知识在工艺文档中的重要性是不同的,如果按照PageRank算法的PR值相等转移来计算工件的重要性,效率和准确性一般较低,因此应考虑PR值转移的重要性差异。因此,本文在PageRank算法中引入K-Shell分解算法,构建新的I-PageRank算法模型,将链接网络中每个节点的重要性加入到PageRank算法中,提高了PageRank算法识别关键节点的效率和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on I-PageRank algorithm model of Process knowledge graph based on K-Shell decomposition algorithm
PageRank algorithm in the calculation of nodes is equally distributed to the node chain of all nodes, but in the actual production of manufacturing enterprises, the importance of process knowledge in process documents is different, if according to the PageRank algorithm PR value equal transfer to calculate the importance of the artifact, efficiency and accuracy is generally low, so the importance of PR value transfer difference should be considered. Therefore, this paper introduces K-Shell decomposition algorithm in PageRank algorithm, constructs a new I-PageRank algorithm model, adding the importance of each node in the linked network to the PageRank algorithm, which improves the efficiency and accuracy of PageRank algorithm in identifying key nodes.
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Icon Arts and Humanities-History and Philosophy of Science
CiteScore
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