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A similarity model based on reinforcement local maximum connected same destination structure oriented to disordered fusion of knowledge graphs
Applied Intelligence ( IF 3.4 ) Pub Date : 2020-04-03 , DOI: 10.1007/s10489-020-01673-9
Lin Lin , Jie Liu , Yancheng Lv , Feng Guo

The alignment and fusion of knowledge graphs have been at entity alignment and fusion which match and align knowledge graphs (KGs) by measuring similarity of the entities in KGs. Nevertheless, false fusion of completely different KGs can be easily caused if only considering entity similarity but ignoring entity relationship similarity. This paper focuses on entity relationship similarity model and KGs fusion method to achieve automatic construction of KGs. First, Same Destination Paths is developed based on Maximum Common Subgraph, which is used to build the Local Maximum Connected Same Destination Structure (LMCSDS) model to measure the entity relationship similarity of KGs. Then, a fusion method for similar fragmentation KGs (FKGs) is developed by analyzing the types of FKGs. Third, a Reinforcement Local Maximum Connected Same Destination Structure (RLMCSDS) similarity model is developed to ensure that the similarity between FKGs can still be measured correctly after fusion of FKGs. Meanwhile, the fusion results obtained by the developed RLMCSDS model and fusion method are theoretically proved to be independent with the fusion order of FKGs. Finally, experimental results on several datasets demonstrate the outstanding performance of the RLMCSDS model in comparison with some existing methods. Moreover, a complete KG can be built by the RLMCSDS model and the fusion method.



中文翻译:

基于面向知识图的无序融合的增强局部最大连接相同目标结构的相似模型

知识图的对齐和融合属于实体对齐和融合,它们通过测量KG中实体的相似性来匹配和对齐知识图(KG)。但是,如果仅考虑实体相似性而忽略实体关系相似性,则很容易导致完全不同的KG的错误融合。本文重点研究实体关系相似模型和KGs融合方法,实现KGs的自动构建。首先,基于“最大公共子图”开发了“相同目的地路径”,该“相同目的地路径”用于构建本地最大连接相同目的地结构(LMCSDS)模型,以测量KG的实体关系相似性。然后,通过分析FKG的类型,开发了一种类似的碎片KG(FKG)的融合方法。第三,建立增强局部最大连接相同目的地结构(RLMCSDS)相似性模型,以确保在融合FKG后仍可以正确测量FKG之间的相似性。同时,理论上证明了通过改进的RLMCSDS模型和融合方法获得的融合结果与FKGs的融合顺序无关。最后,在一些数据集上的实验结果表明,与某些现有方法相比,RLMCSDS模型具有出色的性能。此外,可以通过RLMCSDS模型和融合方法来构建完整的KG。理论上证明了通过改进的RLMCSDS模型和融合方法获得的融合结果与FKG的融合顺序无关。最后,在一些数据集上的实验结果表明,与某些现有方法相比,RLMCSDS模型具有出色的性能。此外,可以通过RLMCSDS模型和融合方法来构建完整的KG。理论上证明了通过改进的RLMCSDS模型和融合方法获得的融合结果与FKG的融合顺序无关。最后,在一些数据集上的实验结果表明,与某些现有方法相比,RLMCSDS模型具有出色的性能。此外,可以通过RLMCSDS模型和融合方法来构建完整的KG。

更新日期:2020-04-03
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