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Title:Autonomous agents for serving complex information needs
Author(s):Sondhi, Parikshit
Director of Research:Zhai, ChengXiang
Doctoral Committee Chair(s):Zhai, ChengXiang
Doctoral Committee Member(s):Roth, Dan; Sun, Jimeng; Schatz, Bruce R.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Autonomous agents
complex information needs
forum search
case retrieval
question answering
knowledge-based Question Resolution (kbqr)
Abstract:Over the past few decades two prominent paradigms for information seeking in the form of search engines and recommendation systems have been developed. However neither of these is well suited to serve queries representing complex information needs (eg. medical case-based queries). As a result users increasingly turn to web communities such as HealthBoards and Yahoo! Answers making them extremely popular. However, not all queries posted there receive informative answers or are answered in a timely manner. In this work we present a novel paradigm for information service in which autonomous agents help dissatisfied users in web communities by proactively posting responses to their unresolved queries. The main contribution of this work is to concretely define three application tasks based on this paradigm in the healthcare domain, and show that it is indeed feasible to develop agents capable of generating meaningful responses with a high accuracy. The first task involved designing an agent for resolving physician case-based queries using literature data. We addressed the problem via methods that utilized available biomedical semantic resources and showed that a precision at 10 of upto 0.48 could be achieved. The second study involved resolving layperson queries on web forums by finding similar discussion threads. This task was more challenging due to noisy nature of forum data and unsuitability of existing semantic resources. We developed novel shallow semantic information extraction techniques for the problem, and our methods utilized them to achieve a best precision at 5 of 0.54. Finally the third task was to design an autonomous agent for resolving general healthcare questions on community question answering (cQA) websites. This task required more detailed semantic information in the form of a database containing precise medical entities, verbose text descriptions, and the relations between them. These were obtained by using health information websites as an information source. We proposed a principled probabilistic model for the problem, and it was found to resolve over 30% of the questions correctly. Overall our results clearly suggest that autonomous agents are not only feasible, but can also deliver considerable value to both expert and layperson users of web forums and cQA websites. We believe such autonomous agents have great potential and our work opens up an exciting new area of research.
Issue Date:2014-01-16
Rights Information:Copyright 2013 Parikshit Sondhi
Date Available in IDEALS:2014-01-16
Date Deposited:2013-12

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