Exploiting Source-Object Networks to Resolve Object Conflicts in Linked Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10249)


Considerable effort has been exerted to increase the scale of Linked Data. However, an inevitable problem arises when dealing with data integration from multiple sources. Various sources often provide conflicting objects for a certain predicate of the same real-world entity, thereby causing the so-called object conflict problem. At present, object conflict problem has not received sufficient attention in the Linked Data community. Thus, in this paper, we firstly formalize the object conflict resolution as computing the joint distribution of variables on a heterogeneous information network called the Source-Object Network, which successfully captures three correlations from objects and Linked Data sources. Then, we introduce a novel approach based on network effects called ObResolution (object resolution), to identify a true object from multiple conflicting objects. ObResolution adopts a pairwise Markov Random Field (pMRF) to model all evidence under a unified framework. Extensive experimental results on six real-world datasets show that our method achieves higher accuracy than existing approaches and it is robust and consistent in various domains.


Linked Data quality Object conflicts Truth discovery 



This work is funded by the National Key Research and Development Program of China (Grant No. 2016YFB1000903), the MOE Research Program for Online Education (Grant No. 2016YB166) and the National Science Foundation of China (Grant Nos. 61370019, 61672419, 61672418, 61532004, 61532015).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.MOEKLINNS LabXi’an Jiaotong UniversityXi’anChina
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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