Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

3-2007

Abstract

User-driven discovery of associations among entities, and documents that provide evidence for these associations, is an important search task conducted by researchers and do-main information specialists. Entities here refer to real or abstract objects such as people, organizations, ideologies, etc. Associations are the inter-relationships among entities. Most current works in query-driven document retrieval and finding representative subgraphs are ill-suited for the task as they lack an awareness of entity types as well as an intuitive representation of associations. We propose the TUBE model, a text cube approach for discovering associations and documentary evidence of these associations. The model consists of a multi-dimensional view of document data, a flexible representation of multi-document summaries, and a set of operations for data manipulation. We conduct a case study on real-life data to illustrate its applicability to the above task and compare it with the non-TUBE approach.

Keywords

association discovery, interactive IR

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

SAC '07: Proceedings of the 2007 ACM Symposium on Applied Computing, Seoul, March 11-15

First Page

824

Last Page

828

ISBN

9781595934802

Identifier

10.1145/1244002.1244185

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1145/1244002.1244185

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