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.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems

Additional Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.3 Communications Applications

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
  I.7 DOCUMENT AND TEXT PROCESSING
      I.7.2 Document Preparation
      I.7.5 Document Capture

General Terms:
Algorithms, Experimentation

Keywords:
information retrieval, knowledge management