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The way in which document collections are generated, modified or updated generates problems and mistakes in the information coherency, leading to legal, economic and social problems. To tackle this situation, this paper proposes the development of an intelligent virtual domain expert, based on summarization, matching and neuro-fuzzy systems, able to detect incoherences about concepts, values, or references, in technical documentation. In this scope, an incoherence is seen as the lack of consistency between related documents. Each document is summarized in the form of 4-tuples terms, describing relevant ideas or concepts that must be free of incoherences. These representations are then matched using several well-known algorithms. The final decision about the real existence of an incoherence, and its relevancy, is obtained by training a neuro-fuzzy system with expert knowledge, based on the previous knowledge of the activity area and domain experts. The final system offers a semi-automatic solution for incoherence detection and decision support.

HYBRID APPROACH FOR INCOHERENCE DETECTION BASED ON NEURO-FUZZY SYSTEMS AND EXPERT KNOWLEDGE