iReMedI- Intelligent Retrieval from Medical Information

Abstract. Effective encoding of information is one of the keys to qualitative problem solving. Our aim is to explore Knowledge representation techniques that capture meaningful word associations occurring in documents. We have developed iReMedI, a TCBR based problem solving system as a prototype to demonstrate our idea. For representation we have used a combination of NLP and graph based techniques which we call as Shallow Syntactic Triples, Dependency Parses and Semantic Word Chains. To test their effectiveness we have developed retrieval techniques based on PageRank, Shortest Distance and Spreading Activation methods. The various algorithms discussed in the paper and the comparative analysis of their results provides us with useful insight for creating an effective problem solving and reasoning system.

• Type: Conference
• Year: 2008
• Where: EWCBR
• Authors: Saurav Sahay, Bharat Ravisekar, Sundaresan Venkatasubramanian, Anushree Venkatesh, Priyanka Prabhu, Ashwin Ram
• Keywords: Automated Reasoning, EWCBR 2008, Knowledge Representation Techniques, Problem Solving, TCBR Based Problem