Title
Estimating similarity among collaboration contributions
Abstract
The need for collaboration arises in many activities required for effective problem solving and decision making. We are developing Angler, a web-services tool that supports collaboration among participants on some focus topic. Angler overcomes some common barriers to collaboration by enabling asynchronous and distributed collaboration. Angler supports a collaboration methodology that exploits opportunities afforded by multiple participants each making contributions to the collaboration. One challenge that arises in helping participants manage their contributions and their review of others' contributions is determining when one contribution is very similar to another contribution. Two very similar contributions may suggest either a need to merge them or to further elaborate one or both of them. Indexes over the participant contributions are used to assess similarity across contributions and address this challenge. The indexes may comprise lexical or ontological information; the former indexes require fewer resources to deploy but the later appear to support better similarity estimates.
Year
DOI
Venue
2005
10.1145/1088622.1088642
International Conference on Knowledge Capture
Keywords
Field
DocType
collaboration contribution,better similarity estimate,common barrier,fewer resource,former index,focus topic,estimating similarity,effective problem,knowledge management,participant contribution,similar contribution,information retrieval,collaboration methodology,enabling asynchronous,web service,indexation
Asynchronous communication,Ontology,Data mining,Computer science,Knowledge management,Exploit,Distributed collaboration,Merge (version control)
Conference
ISBN
Citations 
PageRank 
1-59593-163-5
1
0.38
References 
Authors
7
4
Name
Order
Citations
PageRank
Kenneth S. Murray17813.86
John D. Lowrance2215185.69
Douglas E. Appelt31166326.26
Andres Rodriguez4505.13