@inproceedings{0a332f88a4b748c9a5f7d75a3cc25805,
title = "TruthDiscover: Resolving object conflicts on massive linked data",
abstract = "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.",
keywords = "Linked data, Linked data quality, Object conflicts",
author = "Wenqiang Liu and Jun Liu and Haimeng Duan and Jian Zhang and Wei Hu and Bifan Wei",
note = "Publisher Copyright: {\textcopyright} 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.; 26th International World Wide Web Conference, WWW 2017 Companion ; Conference date: 03-04-2017 Through 07-04-2017",
year = "2017",
doi = "10.1145/3041021.3054722",
language = "英语",
series = "26th International World Wide Web Conference 2017, WWW 2017 Companion",
publisher = "International World Wide Web Conferences Steering Committee",
pages = "243--246",
booktitle = "26th International World Wide Web Conference 2017, WWW 2017 Companion",
}