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A Fuzzy RDF Graph-Matching Method Based on Neighborhood Similarity

A Fuzzy RDF Graph-Matching Method Based on Neighborhood Similarity
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Author(s): Guanfeng Li (Ningxia University, China) and Zongmin Ma (Nanjing University of Aeronautics and Astronautics, China)
Copyright: 2019
Pages: 15
Source title: Emerging Technologies and Applications in Data Processing and Management
Source Author(s)/Editor(s): Zongmin Ma (Nanjing University of Aeronautics and Astronautics, China) and Li Yan (Nanjing University of Aeronautics and Astronautics, China)
DOI: 10.4018/978-1-5225-8446-9.ch009

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Abstract

With the popularity of fuzzy RDF data, identifying correspondences among these data sources is an important task. Although there are some solutions addressing this problem in classical RDF datasets, existing methods do not consider fuzzy information which is an important property existing in fuzzy RDF graphs. In this article, we apply fuzzy graph to model the fuzzy RDF datasets and propose a novel similarity-oriented RDF graph matching approach, which makes full use of the 1-hop neighbor vertex and edge label information, and takes into account the fuzzy information of a fuzzy RDF graph. Based on the neighborhood similarity, we propose a breadth-first branch-and-bound method for fuzzy RDF graph matching, which uses a state space search method and uses truncation parameters to constrain the search. This algorithm can be used to identify the matched pairs.

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