Conference Proceeding Article
To conduct content analysis over text data, one may look out for important named objects and entities that refer to real world instances, synthesizing them into knowledge relevant to a given information seeking task. In this paper, we introduce a visual analytics tool called ER-Explorer to support such an analysis task. ER-Explorer consists of a data model known as TUBE and a set of data manipulation operations specially designed for examining entities and relationships in text. As part of TUBE, a set of interestingness measures is defined to help exploring entities and their relationships. We illustrate the use of ER-Explorer in performing the task of finding associations between two given entities over a text data collection.
Information seeking, Interestingness measures, visual analytics, content analysis, text data
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
IEEE International Conference on Intelligence and Security Informatics, ISI 2008 Workshops: PAISI, PACCF, and SOCO 2008, Taipei, Taiwan; 17 June 2008
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DAI, Hanbo; LIM, Ee Peng; LAUW, Hady W.; and PANG, Hwee Hwa.
Visual Analytics for Supporting Entity Relationship Discovery on Text. (2008). IEEE International Conference on Intelligence and Security Informatics, ISI 2008 Workshops: PAISI, PACCF, and SOCO 2008, Taipei, Taiwan; 17 June 2008. 5075, 183-194. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/292
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.