Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/940
Title: Graph-based cluster analysis to identify similar questions: a design science approach.
Epworth Authors: Wickramasinghe, Nilmini
Other Authors: John, Blooma
Goh, Dion
Chua, Alton
Keywords: Cluster Analysis
Graph Theory
Design Science
Social Question Answering
SQA
Responsiveness
Chair of Health Informatics Management, Epworth HealthCare, Victoria, Australia
Issue Date: Sep-2016
Publisher: Association for Information Systems
Citation: JAIS, Sep 2016; 17(9): 590-613
Abstract: Social question answering (SQA) services allow users to clarify their queries by asking questions and obtaining answers from other users. To enhance the responsiveness of such services, one can identify similar questions and, thereafter, return the answers available. However, identifying similar questions is difficult because of the complex language structure of user-generated questions. For this reason, we developed an approach to cluster similar questions based on a web of social relationships among the questions, the answers, the askers, and the answerers. To do so, we designed a graph-based cluster analysis using design science research guidelines. In evaluating the results, we found that the proposed graph-based cluster analysis is more promising than baseline methods.
URI: http://hdl.handle.net/11434/940
URL: http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1751&context=jais
ISSN: 1536-9323
Journal Title: Journal of the Association for Information Systems
Type: Journal Article
Type of Clinical Study or Trial: Exploratory Qualitative Design
Appears in Collections:Health Informatics

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