  Considerable effort has been made to increase the scale of Linked Data.
However, an inevitable problem when dealing with data integration from multiple
sources is that multiple different sources often provide conflicting objects
for a certain predicate of the same real-world entity, so-called object
conflicts problem. Currently, the object conflicts problem has not received
sufficient attention in the Linked Data community. In this paper, we first
formalize the object conflicts resolution problem as computing the joint
distribution of variables on a heterogeneous information network called the
Source-Object Network, which successfully captures the all 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 evidences under a unified framework. Extensive
experimental results on six real-world datasets show that our method exhibits
higher accuracy than existing approaches and it is robust and consistent in
various domains. \keywords{Linked Data, Object Conflicts, Linked Data Quality,
Truth Discovery
