Machine Learning and Conceptual Reasoning for Inconsistency Detection

This paper focuses on detecting inconsistencies within text corpora. It is a very interesting area with many applications. Most existing methods deal with this problem using complicated textual analysis, which is known for not being accurate enough. We propose a new methodology that consists of two steps, the first one being a machine learning step that performs multilevel text categorization. The second one applies conceptual reasoning on the predicted categories in order to detect inconsistencies. This paper has been validated on a set of Islamic advisory opinions (also known as fatwas). This domain is gaining a large interest with users continuously checking the authenticity and relevance of such content. The results show that our method is very accurate and can complement existing methods using the linguistic analysis.