iReMedI - Intelligent Retrieval from Medical Information

Authors:
Saurav Sahay;Bharat Ravisekar;Sundaresan Venkatasubramanian;Anushree Venkatesh;Priyanka Prabhu;Ashwin Ram
Affiliations:
College of Computing, Georgia Institute of Technology, Atlanta,;College of Computing, Georgia Institute of Technology, Atlanta,;College of Computing, Georgia Institute of Technology, Atlanta,;College of Computing, Georgia Institute of Technology, Atlanta,;College of Computing, Georgia Institute of Technology, Atlanta,;College of Computing, Georgia Institute of Technology, Atlanta,
Venue:
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Year:
2008

Citing 7
Cited 0

The synergistic application of CBR to IR

Artificial Intelligence Review
The anatomy of a large-scale hypertextual Web search engine

WWW7 Proceedings of the seventh international conference on World Wide Web 7
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System

Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Textual case-based reasoning

The Knowledge Engineering Review
Conceptual graphs for a data base interface

IBM Journal of Research and Development
Document mining based on semantic understanding of text

CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Reasoning with textual cases

ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development

Quantified Score

Hi-index	0.00

Visualization

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.