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-  2018 


DOI: 10.13543/j.bhxbzr.2018.01.012

Keywords: 关联开放数据,语义网,机器学习,VS-Adaboost,
linked open data
,semantic web,machine learning,VS-Adaboost

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Abstract:Entity alignment is the key technology needed to realize knowledge graph construction, information integration and sharing. Most of the existing entity alignment approaches are based on ontology pattern matching, and have limitations when dealing with the alignment of heterogeneous linked open data(LOD), where the problem of missing entity links is serious. In this paper, a schema-independent entity alignment approach based on attribute semantic features is proposed. The entity attributes of the LOD are modeled according to their semantic label characteristics and statistical features. The supervised variable set VS-Adaboost algorithm is used to realize classifier optimization. The experiments executed on selected datasets show that compared with conventional methods the approach is more efficient and has better efficacy in terms of accuracy, recall and F measurement.


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