Visual analytics for supporting entity relationship discovery on text data

Visual analytics for supporting entity relationship discovery on text data

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 s...

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Bibliographic Details
Main Authors: DAI, Hanbo, LIM, Ee Peng, LAUW, Hady W., PANG, Hwee Hwa
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2008
Subjects:
Information seeking
Interestingness measures
visual analytics
content analysis
text data
Databases and Information Systems
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/292
https://ink.library.smu.edu.sg/context/sis_research/article/1291/viewcontent/Visual_Analytics_for_Supporting_Entity_Relationship_Discovery_on_Text__edited_.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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