Abstract
Increasing concerns about the prevalence of false information and fake news has led to calls for automated fact-checking systems that are capable of verifying the truthfulness of statements, especially on the internet. Most previous automated fact-checking systems have focused on the use of grammar rules only for determining the properties of the language used in statements. Here, we demonstrate a novel approach to the fact-checking of natural language text, which uses a combination of all the following techniques: knowledge extraction to establish a knowledge base, logical inference for fact-checking of claims not explicitly mentioned in the text through the verification of the consistency of a set of beliefs with established trusted knowledge, and a re-querying approach that enables continuous learning. The approach that is presented here addresses the limitations of existing automated fact-checking systems via this novel procedure. This procedure is as follows: the approach investigates the consistency of presented facts or claims while using probabilistic soft logic and a Knowledge Base, which is continuously updated through continuous learning strategies. We demonstrate this approach by focusing on the task of checking facts about family-tree relationships against a corpus of web resources concerned with the UK Royal Family.
Highlights
Information available on the internet is being claimed, rightly or wrongly, to be false
We describe Facts Automatic Consistency Test (FACT) as a system that builds a knowledge base using an information extraction tool for extracting constants, their properties, and their relations, with a given domain, and fact-checking any given claims or statements by establishing their consistency with this knowledge base
(Section 3), we introduce Probabilistic Soft Logic (PSL) and its underlying method that is based on Hinge-Loss Markov Random Field (HL-Markov Random Field (MRF))
Summary
Information available on the internet is being claimed, rightly or wrongly, to be false. Traditional, manual approaches to verifying the truth of statements are often no longer applicable, because of the sheer amount of information being published on the internet. Recognition of this problem has prompted research into and the development of, automated computational approaches to the identification of potentially false information. The underlying aim of FACT is to minimize inconsistency, as determined by inference using soft logic [7] This requires information extraction, and the application of logical inference to check the claim. This approach contrasts with those that study the subjectivity of language in order to detect such a thing as propaganda, satire, and hoaxes [5] or speculation [6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
