Mining latent entity structures from massive unstructured and interconnected data

Wang, Chi

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https://hdl.handle.net/2142/72967 Copy

Description

Title

Mining latent entity structures from massive unstructured and interconnected data

Author(s)

Wang, Chi

Issue Date

2015-01-21

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

Han, Jiawei

Doctoral Committee Chair(s)

Han, Jiawei

Committee Member(s)

• Zhai, ChengXiang
• Roth, Dan
• Chakrabarti, Kaushik

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• data mining
• text mining
• information network
• social network
• network analysis
• probabilistic graphical model
• topic model
• phrase mining
• relation mining
• Information Extraction

Abstract

The “big data” era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone’s daily life. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured but interconnected data. Mining latent structured information around entities uncovers semantic structures from massive unstructured data and hence enables many high-impact applications, including taxonomy or knowledge base construction, multi-dimensional data analysis and information or social network analysis. A mining framework is proposed, to solve and integrate a chain of tasks: hierarchical topic discovery, topical phrase mining, entity role analysis and entity relation mining. It reveals two main forms of structures: topical and relational structures. The topical structure summarizes the topics associated with entities with various granularity, such as the research areas in computer science. The framework enables recursive construction of phrase-represented and entity-enriched topic hierarchy from text-attached information networks. It makes breakthrough in terms of quality and computational efficiency. The relational structure recovers the hidden relationship among entities, such as advisor-advisee. A probabilistic graphical modeling approach is proposed. The method can utilize heterogeneous attributes and links to capture all kinds of semantic signals, including constraints and dependencies, to recover the hierarchical relationship with the best known accuracy.

Graduation Semester

2014-12

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http://hdl.handle.net/2142/72967

Copyright and License Information

Copyright 2014 Chi Wang

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