TUBE (Text-cUBE) for discovering documentary evidence of associations among entities

Author

Hady LAUW, Singapore Management UniversityFollow
Ee Peng LIM, Singapore Management UniversityFollow
Hwee Hwa PANG, Singapore Management UniversityFollow

Publication Type

Conference Proceeding Article

Version

acceptedVersion

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

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

Citation

LAUW, Hady; LIM, Ee Peng; and PANG, Hwee Hwa. TUBE (Text-cUBE) for discovering documentary evidence of associations among entities. (2007). SAC '07: Proceedings of the 2007 ACM Symposium on Applied Computing, Seoul, March 11-15. 824-828.
Available at: https://ink.library.smu.edu.sg/sis_research/372

Creative Commons License

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

Additional URL

https://doi.org/10.1145/1244002.1244185