Conference Proceeding Article
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.
association discovery, interactive IR
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Applied Computing 2007: Proceedings of the 2007 ACM Symposium on Applied Computing, Seoul, Korea, 11-15 March
City or Country
LAUW, Hady; LIM, Ee Peng; and PANG, Hwee Hwa.
TUBE (Text-cUBE) for discovering documentary evidence of associations among entities. (2007). Applied Computing 2007: Proceedings of the 2007 ACM Symposium on Applied Computing, Seoul, Korea, 11-15 March. 824-828. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/372
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