Claim Consistency Checking Using Soft Logic

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.