Linked Data Integration with Conflicts

Michelfeit, Jan; Knap, Tomáš; Nečaský, Martin

doi:10.48550/arxiv.1410.7990

Cited by 3 publications

(5 citation statements)

References 15 publications

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“…ACCUCOPY considers the copying relationships between the sources, the accuracy of data sources, and the similarity between values. -F-Quality Assessment [15]. This method is a popular algorithm used to resolve conflicts in Linked Data.…”

Section: Comparative Methods and Metricsmentioning

confidence: 99%

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

Liu

Duan

et al. 2017

The Semantic Web

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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.

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Section: Comparative Methods and Metricsmentioning

confidence: 99%

“…-The conflict-resolution strategy focuses on how to solve a conflict regarding the characteristics of all data and metadata. For example, Michelfeit et al [15] presented an assessment model that leverages the quality of the source, data conflicts, and confirmation of values for determining which value should be the true value.…”

Section: Related Workmentioning

confidence: 99%

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

Liu

Duan

et al. 2017

The Semantic Web

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“…The method we choose is that all objects linked by comparable predicates are regarded as a set of mutually fusible object conflict sets. The cluster composed of these sets is also called object conflict cluster [5]. After constructing the cluster, our ultimate goal is to traverse all object conflict sets in the cluster, implement conflict resolution strategies for each set, and find the true value in the set.…”

Section: Rdf Object Conflict Clustermentioning

confidence: 99%

“…The fusion strategy can be used to solve the possible conflicts and inequalities at the text level. Based on some existing common conflict resolution strategies [5], we have added some more complete and applicable strategies. They are:…”

Section: Conflict Resolution For Literal Nodesmentioning

confidence: 99%

Linked Data Fusion Based on Similarity Calculation and K-Nearest Neighbor

Chen

2022

J. Phys.: Conf. Ser.

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The development of semantic web technology supports the continuous development of linked data and its applications. In order to make effective use of the growing linked data on the web, multi-source data should be fused, which is a key step in multi-source large-scale data analysis and management. Currently, merging these data can be difficult, because various sources usually provide multiple conflict descriptions for entities in the same real world. To complete the fusion, we need to solve the problem of object conflict. This paper proposes a linked data fusion method based on similarity calculation and k-nearest neighbor. This method has two contributions. Firstly, a similarity calculation method of linked data is proposed, which can effectively integrate URI nodes and blank nodes in linked data; Secondly, a literal type node fusion strategy based on k-nearest neighbor classification method is proposed, which realizes the automation of fusion and has the independence of data source. The results show that compared with other methods, this method can improve the conciseness and consistency and precision by up to 12.9%, 30.6% and 12%.

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“…The statistics of the four datasets are shown in Table 2. We select three well-known state-of-the-art truth discovery methods as baseline including Vote, TruthFinder [8] and F-Quality Assessment [6]. The parameters of the baseline methods are set according to the authors's suggestions.…”

Section: Experimental Evaluationmentioning

confidence: 99%

TruthDiscover

Liu

Duan

et al. 2017

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

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Considerable effort has been made to increase the scale of Linked Data. However, because of the openness of the Semantic Web and the ease of extracting Linked Data from semi-structured sources (e.g., Wikipedia) and unstructured sources, many Linked Data sources often provide conflicting objects for a certain predicate of a real-world entity. Existing methods cannot be trivially extended to resolve conflicts in Linked Data because Linked Data has a scale-free property. In this demonstration, we present a novel system called TruthDiscover, to identify the truth in Linked Data with a scale-free property. First, TruthDiscover leverages the topological properties of the Source Belief Graph to estimate the priori beliefs of sources, which are utilized to smooth the trustworthiness of sources. Second, the Hidden Markov Random Field is utilized to model interdependencies among objects for estimating the trust values of objects accurately. TruthDiscover can visualize the process of resolving conflicts in Linked Data.

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Linked Data Integration with Conflicts

Cited by 3 publications

References 15 publications

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

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

Linked Data Fusion Based on Similarity Calculation and K-Nearest Neighbor

TruthDiscover

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