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

Abstract

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 Fusion Based on Similarity Calculation and K-Nearest Neighbor

“…We selected three popular RDF data fusion systems for comparative experiments. They are ObResolution [2], Truth Discovery [11], MINTE [12]. Our method is called RDFSim+KNN.…”

“…Our task is to map the already formed images in IM Data fusion is carried out to form a complete and brief centralized data set. We from IM@OAEI (2010) 1 and IM@OAEI(2019) 2 has built two data sets with two types: restaurant and creative work. Each type of data has two heterogeneous data sets, and our task is to fuse the two data sets in each type.…”

“…We need to find the conflicting data in the adjacency graph and resolve the conflict. The solution of this problem is also called object conflict resolution [2].…”

TruthDiscover

“…However, the effectiveness of existing truth discovery methods is significantly affected by the number of objects provided by each source. In our previous work [5], we found that the number of conflicting objects provided by most of the sources ranges from 1 to 10, and only a few sources have many conflicting objects. This finding indicates that Linked Data has a scale-free property.…”

“…Section 5 presents the conclusions of this demonstration. Source Belief Graph [5]: Given a SameAs Graph SG, the Source Belief Graph can be denoted by SBG = (W, R), where W is a set of vertices with each vertex corresponding to the source name of the vertex in SameAs Graph SG; R is a multiset of W × W formed by pairs of vertices (µ, ν), µ, ν ∈ W and each pair (µ, ν) corresponds to an edge in SameAs Graph SG.…”