KNOWLEDGE BASE REFINEMENT WITH INTERNAL AND EXTERNAL DATA

PhD Thesis Proposal Defence


Title: "KNOWLEDGE BASE REFINEMENT WITH INTERNAL AND EXTERNAL DATA"

by

Mr. Hao XIN


Abstract:

In the contemporary digital age, a multitude of publicly accessible knowledge 
bases (KBs) have been established to bolster knowledge-centric applications 
such as search engines and online recommendations.

Nonetheless, these knowledge bases grapple with the issue of incomplete 
knowledge. Firstly, certain domain-specific knowledge remains relatively 
uncharted. For instance, current KBs primarily concentrate on encoding factual 
data, considered as objective knowledge. Secondly, in dynamic real- world 
scenarios where information is constantly evolving, these KBs struggle to keep 
up with emerging data, resulting in incomplete databases.

In this thesis, we investigate the knowledge base refinement task from both 
external and internal data sources, which contains three major research 
problems.

Firstly, we tackle the issue of enriching subjective domain knowledge, aiming 
to bridge the gap between existing KBs and subjective knowledge. We propose a 
framework for enriching knowledge bases with subjective knowledge, leveraging 
knowledge from the crowd and existing KBs.

Secondly, we examine the problem of populating knowledge bases, which involves 
extracting knowledge from unstructured text that aligns with the schema of the 
target KBs, thereby enriching them. We propose a comprehensive system that 
inputs an incomplete target KB and documents, and outputs concise triples. It 
initially performs joint entity and relation linking to the existing KB based 
on both the context of the document and background KB information. It then 
summarizes the extracted facts by considering their relevance to the document 
and the diversity among them.

Thirdly, we investigate the issue of updating knowledge bases, which involves 
identifying and updating outdated facts in KBs. We employ the revision history 
of the target KB to learn how to identify outdated facts and propose a 
cost-aware fact selection algorithm to guide the fact update process. 
Furthermore, we explore the problem of Knowledge Update Rule Discovery (KURD), 
which seeks to derive an optimal subset of knowledge update rules for 
performing knowledge updating, taking into account rule quality and coverage.

We validate the effectiveness and efficiency of the proposed solutions for each 
of the aforementioned problems against cutting-edge techniques, through 
comprehensive experiments on real-world datasets. Finally, we conclude the 
thesis by outlining future research directions and challenges pertaining to the 
task of refining knowledge bases.


Date:                   Friday, 10 May 2024

Time:                   2:00pm - 4:00pm

Venue:                  Room 4475
                        Lifts 25/26

Committee Members:      Prof. Lei Chen (Supervisor)
                        Prof. Qiong Luo (Chairperson)
                        Prof. Ke Yi
                        Dr. Nan Tang (HKUST-GZ)