COMPLEX QUESTION ANSWERING BASED ON A SEMANTIC DOMAIN MODEL OF CLINICAL MEDICINE

Much research in recent years has focused on question answering. Due to significant advances in answering simple fact-seeking questions, research is moving towards resolving complex questions. An approach adopted by many researchers is to decompose a complex question into a series of fact-seeking questions and reuse techniques developed for answering simple questions. This thesis presents an alternative novel approach to domain-specific complex question answering based on consistently applying a semantic domain model to question and document understanding as well as to answer extraction and generation. This study uses a semantic domain model of clinical medicine to encode (a) a clinician's information need expressed as a question on the one hand and (b) the meaning of scientific publications on the other to yield a common representation. It is hypothesized that this approach will work well for (1) finding documents that contain answers to clinical questions and (2) extracting these answers from the documents. The domain of clinical question answering was selected primarily because of its unparalleled resources that permit providing a proof by construction for this hypothesis. In addition, a working prototype of a clinical question answering system will support research in informed clinical decision making. The proposed methodology is based on the semantic domain model developed within the paradigm of Evidence Based Medicine. Three basic components of this model - the clinical task, a framework for capturing a synopsis of a clinical scenario that generated the question, and strength of evidence presented in an answer - are identified and discussed in detail. Algorithms and methods were developed that combine knowledge-based and statistical techniques to extract the basic components of the domain model from abstracts of biomedical articles. These algorithms serve as a foundation for the prototype end-to-end clinical question answering system that was built and evaluated to test the hypotheses. Evaluation of the system on test collections developed in the course of this work and based on real life clinical questions demonstrates feasibility of complex question answering and high accuracy information retrieval using a semantic domain model

Using ontology in query answering systems: Scenarios, requirements and challenges

Equipped with the ultimate query answering system, computers would finally be in a position to address all our information needs in a natural way. In this paper, we describe how Language and Computing nv (L&C), a developer of ontology-based natural language understanding systems for the healthcare domain, is working towards the ultimate Question Answering (QA) System for healthcare workers. L&C’s company strategy in this area is to design in a step-by-step fashion the essential components of such a system, each component being designed to solve some one part of the total problem and at the same time reflect well-defined needs on the prat of our customers. We compare our strategy with the research roadmap proposed by the Question Answering Committee of the National Institute of Standards and Technology (NIST), paying special attention to the role of ontology

Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks.

BackgroundUsing knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms?ResultsOur existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.ConclusionsWith some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels