TruthDiscover: Resolving Object Conflicts on Massive Linked Data

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. Experiments results on four datasets show that TruthDiscover exhibits satisfactory accuracy when confronted with data having a scale-free property.Comment: This paper had been accepted by Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 2017, WWW201

Truth Discovery to Resolve Object Conflicts in Linked Data

In the community of Linked Data, anyone can publish their data as Linked Data on the web because of the openness of the Semantic Web. As such, RDF (Resource Description Framework) triples described the same real-world entity can be obtained from multiple sources; it inevitably results in conflicting objects for a certain predicate of a real-world entity. The objective of this study is to identify one truth from multiple conflicting objects for a certain predicate of a real-world entity. An intuitive principle based on common sense is that an object from a reliable source is trustworthy; thus, a source that provide trustworthy object is reliable. Many truth discovery methods based on this principle have been proposed to estimate source reliability and identify the truth. However, the effectiveness of existing truth discovery methods is significantly affected by the number of objects provided by each source. Therefore, these methods cannot be trivially extended to resolve conflicts in Linked Data with a scale-free property, i.e., most of the sources provide few conflicting objects, whereas only a few sources have many conflicting objects. To address this challenge, we propose a novel approach called TruthDiscover to identify the truth in Linked Data with a scale-free property. Two strategies are adopted in TruthDiscover to reduce the effect of the scale-free property on truth discovery. First, this approach 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, this approach utilizes the Hidden Markov Random Field to model the interdependencies between objects to estimate the trust values of objects accurately. Experiments are conducted in the six datasets to evaluate TruthDiscover.Comment: Have many crucial faults in this versio

A new approach for interlinking and integrating semi-structured and linked data

This work focuses on improving data integration and interlinking systems targeting semi-structured and Linked Data. It aims at facilitating the exploitation of semi-structured and Linked Data by addressing the problems of heterogeneity, complexity, scalability and the degree of automation. Technologies, such as the Resource Description Framework (RDF), enabled new data spaces and concept descriptors to define an increasing complex and heterogeneous web of data. Many data providers, however, continue to publish their data using classic models and formats. In addition, a significant amount of the data released before the existence of the Linked Data movement have not emigrated and still have a high value. Hence, as a long term solution, an interlinking system has been designed to contribute to the publishing of semi-structured data as Linked Data. Simultaneously, to utilise these growing data resource spaces, a data integration middleware has been proposed as an immediate solution. The proposed interlinking system verifies in the first place the existence of the Uniform Resource Identifier (URI) of the resource being published in the cloud in order to establish links with it. It uses the domain information in defining and matching the datasets. Its main aim is facilitating following best practice recommendations in publishing data into the Linked Data cloud. The results of this interlinking approach show that it can target large amounts of data whilst preserving good precision and recall. The new approach for integrating semi-structured and Linked Data is a mediator-based architecture. It enables the integration, on-the-fly, of semi-structured heterogeneous data sources with large-scale Linked Data sources. Complexity is tackled through a usable and expressive interface. The evaluation of the proposed architecture shows high performance, precision and adaptability