Integrating multiple conflicting sources by truth discovery and source quality estimation

Zhi, Shi

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https://hdl.handle.net/2142/50493 Copy

Description

Title

Integrating multiple conflicting sources by truth discovery and source quality estimation

Author(s)

Zhi, Shi

Issue Date

2014-09-16

Director of Research (if dissertation) or Advisor (if thesis)

Han, Jiawei

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Truth Discovery
• Data Integration
• Data Quality

Abstract

Multiple descriptions about the same entity from different sources will inevitably result in data or information inconsistency. Among conflicting pieces of information, which one is the most trustworthy? How to detect the fraudulence of a rumor? Obviously, it is unrealistic to curate and validate the trustworthiness of every piece of information because of the high cost of human labeling and lack of experts. To find the truth of each entity, much research work has shown that considering the quality of information providers can improve the performance of data integration. Due to different quality of data sources, it is hard to find a general solution that works for every case. Therefore, we start from a general setting of truth analysis at first and narrow down to two basic problems in data integration. We first propose a general framework to deal with numerical data with flexibility of defining loss function. Source quality is represented by a vector to model the source credibility in different error interval. Then we propose a new method called No Truth Truth Model(NTTM) to deal with truth existence problem in low-quality data. Preliminary experiments on real stock data and slot filling data show promising results.

Graduation Semester

2014-08

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http://hdl.handle.net/2142/50493

Copyright and License Information

Copyright 2014 Shi Zhi

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