Due to the complicated nature of medical information needs, the time constraints of clinicians, and the linguistic complexities and sheer volume of medical information, most medical questions go unanswered. It has been shown that nearly all of these questions can be answered with the presently available medical sources and that when these questions get answered, patient health benefits. In this work, we design and describe a framework for Evidence-Based medical information research and delivery, MedicInfoSys. This system leverages the strengths of knowledge-based workers and of mature knowledge-based technologies within the medical domain. The most critical element of this framework, is a search interface, PifMed. PifMed uses gold-standard MeSH categorization (presently integrated into medline) as the basis of a navigational structure, which allows users to browse search results with an interactive tree of categories. Evaluation by user study shows it to be superior to PubMed, in terms of speed and usability.

Word Sense Disambiguation (WSD) continues to be an open research problem in spite of recent advances in the NLP field, especially in machine learning. WSD for open-class words is well understood. However, WSD for closed class structural words (such as prepositions) is not so well resolved, and their role in frame semantics seems to be a relatively unknown area. This research uses a new method to disambiguate preposition senses by using a combined lookup from FrameNet and TPP databases. Motivated by recent work by Popescu, Tonelli, & Pianta (2007), it extends the concept to provide a deterministic WSD of prepositions using the lexical information drawn from the sentences in a local context. While the primary goal of the research is to disambiguate preposition sense, the approach also assigns frames and roles to different sentence elements. The use of prepositions for frame and role assignment seems to be a largely unexplored area which could provide a new dimension to research in lexical semantics.

No / A genetic algorithm (GA) augmented with knowledge-based methods has been developed for solving the unit commitment economic dispatch problem. The GA evolves a population of binary strings which represent commitment schedules. The initial population of schedules is chosen using a method based on elicited scheduling knowledge. A fast rule-based dispatch method is then used to evaluate candidate solutions. The knowledge-based genetic algorithm is applied to a test system of ten thermal units over 24-hour time intervals, including minimum on/off times and ramp rates, and achieves lower cost solutions than Lagrangian relaxation in comparable computational time.