Semantic inference based on ontology for medical FAQ mining

We present an approach to semantic inference for FAQ mining based on ontology. The questions are classified into ten intension categories using predefined question stemming keywords. The answers in the FAQ database are also clustered using latent semantic analysis (LSA) and K-means algorithm. For FAQ mining, given a query, the question part and answer part in an FAQ question-answer pair is matched with the input query, respectively. Finally, the probabilities estimated from these two parts are integrated and used to choose the most likely answer for the input query. These approaches are experimented on a medical FAQ system. The results show that the proposed approach achieved a retrieval rate of 90% and outperformed the keyword-based approach.