Combining Truth Discovery and RDF Knowledge Bases to their mutual advantage

author: Valentina Beretta, IMT Mines Ales
published: Nov. 22, 2018,   recorded: October 2018,   views: 252


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


This study exploits knowledge expressed by RDF Knowledge Bases (KBs) to enhance Truth Discovery performance. Truth Discovery aims to identify facts (true claims) when conflicting claims are provided by several sources. Based on the assumption that true claims are provided by reliable sources and reliable sources provide true claims, Truth Discovery models iteratively compute value confidence and source trustworthiness in order to determine which claims are true. We propose a model that takes advantage of the knowledge extracted from an existing RDF KB in form of rules. These rules are used to quantify the evidence given by the RDF KB to support a claim. Then, this evidence is integrated in the computation of value confidence to improve its estimation. Enhancing truth discovery models allows to efficiently obtain a larger set of reliable facts that vice versa can be used to populate RDF KBs. Empirical experiments on real-world datasets show the potential of the proposed approach which lead to an improvement up to 18% w.r.t. the model we modified.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: