Title
A Similarity Graph Matching Approach for Instance Disambiguation
Abstract
Instance matching acts as a significant part of information integration in semantic web research. While ontology matching focuses on the schema level of data, instance matching deals with massive instances objects. Ambiguation is a common problem which may lead to error matching when different instances share the same names or descriptions. To cope with this problem structural approach is used by many matching systems for disambiguation. However, existing structural approach has a hidden problem named 'error propagation' which would affect the precision of matching result. In this paper, we investigate instance matching techniques and propose a new instance matching framework. It is based on a novel structural matching algorithm which calculates similarity separately on sub graphs. The structural information is fully taken advantage of to realize disambiguation and several indexing strategies are used to cut down the computing overhead. We have conducted experiments on instance matching benchmark and results show that our proposed matching approach is comparable to state-of-art systems. And experiment on real dataset has proved the validity of our approach in instance disambiguation.
Year
DOI
Venue
2016
10.1109/ES.2016.9
2016 4th International Conference on Enterprise Systems (ES)
Keywords
Field
DocType
instance matching framework,error propagation,massive instances objects,ontology matching,semantic Web research,information integration,instance disambiguation,similarity graph matching approach
Ontology (information science),Information integration,Ontology alignment,Data mining,Optimal matching,Computer science,Search engine indexing,Matching (graph theory),3-dimensional matching,Blossom algorithm
Conference
ISBN
Citations 
PageRank 
978-1-5090-3796-4
0
0.34
References 
Authors
6
6
Name
Order
Citations
PageRank
Haojian Zhong100.34
Lida Xu26275279.34
Cheng Xie316215.84
Boyi Xu426120.53
Fenglin Bu520013.06
Hongming Cai639658.68