Knowledge Base Construction Powered By Hybrid Human-Machine Computation

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Knowledge Base Construction Powered By Hybrid Human-Machine 
Computation"

By

Miss Rui MENG


Abstract

Recent years have witnessed a growing interest in both academia and 
industry for knowledge base construction (KBC). Knowledge base 
construction (KBC) refers to the process of populating a knowledge base 
(KB) with facts (or assertions) extracted from information sources, 
including documents, books, sensors, human, etc. While notable efforts 
have been devoted, the state-of-the-art automatic KBC techniques, which 
rely on the information extraction (IE), natural language processing (NLP) 
and machine learning approaches, still have its limitations and can yield 
noisy or semantically meaningless knowledge facts. Human computation and 
crowdsourcing are becoming ever more popular paradigms in computing which 
employ the power of human knowledge and expertise to handle tasks that are 
difficult for machines to handle alone. Crowdsourcing offers an 
alternative approach for KBC in which the crowd power can be incorporated 
to refine the knowledge extraction and acquisition process; moreover, as a 
natural source of knowledge, the crowd can be mined to obtain knowledge 
that resides in the human mind. However, the crowd alone cannot carry the 
whole burden of KBC due to the conflict between limited crowdsourcing 
resource and the large scales of real KBs.

In this thesis, to address the shortcomings of both automatic and human 
computation approaches, we propose hybrid human-machine computation 
frameworks for KBC to complement automatic knowledge base construction 
with the wisdom and power of the crowd. To summarize, our study address 
the following problems:

·        We propose a hybrid framework to combine the crowd and machine 
intelligence for taxonomy construction towards both high accuracy and high 
coverage.

·        We study the problem of KBC by integrating existing large scale 
KBs in the new crowdsourcing perspective.

·        We identify a subjective KBC problem which targets at subjective 
knowledge acquisition. We present two hybrid frameworks for subjective KBC 
powered by crowdsourcing and existing KBs.

We verify the effectiveness of the proposed frameworks with extensive 
experiments on real data sets and crowdsourcing platforms. In the end, we 
discuss future research direction of KBC with hybrid human-machine 
computation.


Date:			Monday, 21 August 2017

Time:			10:00am - 12:00noon

Venue:			Room 2611
 			Lifts 31/32

Chairman:		Prof. Yang Yang (MATH)

Committee Members:	Prof. Lei Chen (Supervisor)
 			Prof. Qiong Luo
 			Prof. Huamin Qu
 			Prof. Xianhua Peng (MATH)
 			Prof. Jian Pei (Comp. Sci., Simon Fraser Univ)


**** ALL are Welcome ****