Multi-dimensional mining of unstructured data with limited supervision

As one of the most important data forms, unstructured text data plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to healthcare and scientific research. In many emerging applications, people's information needs from text data are becoming multi-dimensional---they demand useful insights for multiple aspects from the given text corpus. However, turning massive text data into multi-dimensional knowledge remains a challenge that cannot be readily addressed by existing data mining techniques. In this thesis, we propose algorithms that turn unstructured text data into multi-dimensional knowledge with limited supervision. We investigate two core questions: 1. How to identify task-relevant data with declarative queries in multiple dimensions? 2. How to distill knowledge from data in a multi-dimensional space? To address the above questions, we propose an integrated cube construction and exploitation framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multi-dimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling multi-dimensional knowledge from data to provide insights along multiple dimensions. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multi-dimensional, multi-granular data selection with declarative queries; and with cube exploitation algorithms, users can make accurate cross-dimension predictions or extract multi-dimensional patterns for decision making. The proposed framework has two distinctive advantages when turning text data into multi-dimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multi-dimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multi-dimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Zhang, Chao

Contributors dc:contributor

• Han, Jiawei
• Zhai, ChengXiang
• Abdelzaher, Tarek
• Mei, Qiaozhu

• data mining
• multi-dimensional analysis
• less supervision

Statement dc:rights

• Copyright 2018 Chao Zhang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/102465

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/102465