a framework of a logic-based question-answering system for the medical domain (loqas-med)
Question-answering systems that provide precise answers to questions, by combining techniques for information retrieval, information extraction, and natural language processing, are seen as the next-generation search engines. Due to the growth and real-world impact of biomedical information, the need for question-answering systems that can aid medical researchers and health care professionals in their information search is acutely felt. In order to provide users with accurate answers, such systems need to go beyond lexico-syntactic analysis to semantic analysis and processing of texts and knowledge resources. Moreover, question-answering systems equipped with reasoning capabilities can derive more adequate answers by using inference. Research on question answering in the medical and health care domain is still in its inception stage. While several recent approaches to medical question answering have explored use of semantic knowledge, few approaches have exploited the utility of logic formalisms and of inference mechanisms. In this paper, we present a framework for a logic-based question-answering system for the medical domain, which uses Description Logic as the formalism for knowledge representation and reasoning. As a first step toward building the proposed system, we present semantic analysis and classification of medical questions.
natural language processing question answering description logics biomedical informatics semantic text mining