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With the increasing availability of large-scale structured knowledge bases and natural language processing (NLP) techniques aided by advanced information retrieval (IR) techniques, question answering (QA) systems have entered into a commercialization era. However, the types of questions that can be answered are somewhat limited to encyclopedic knowledge that are often either well structured like triplets or localized in a text segment. In this work, we propose a conceptual graph based question answering (CGQA) framework that enables informal inference and context-driven knowledge representation. This approach has been implemented with NLP techniques for generating conceptual graphs from text and efficient graph matching algorithms as an inference mechanism, which is geared toward answering not only conventional but also 'hard' questions. MOTS-CLÉS: graphique conceptuel, cadre de questions réponses, appariement graphique

A New Question Answering Approach with Conceptual Graphs