Publication Date
2018
Document Type
Thesis
Committee Members
Derek Doran (Committee Member), Cory Henson (Committee Member), Saeedeh Shekarpour (Committee Member), Amit Sheth (Advisor), Krishnaprasad Thirunarayan (Committee Member)
Degree Name
Doctor of Philosophy (PhD)
Abstract
Domain knowledge plays a significant role in powering a number of intelligent applications such as entity recommendation, question answering, data analytics, and knowledge discovery. Recent advances in Artificial Intelligence and Semantic Web communities have contributed to the representation and creation of this domain knowledge in a machine-readable form. This has resulted in a large collection of structured datasets on the Web which is commonly referred to as the Web of data. The Web of data continues to grow rapidly since its inception, which poses a number of challenges in developing intelligent applications that can benefit from its use. Majority of these applications are focused on a particular domain. Hence they can benefit from a relevant portion of the Web of Data. For example, a movie recommendation application predominantly requires knowledge of the movie domain and a biomedical knowledge discovery application predominantly requires relevant knowledge on the genes, proteins, chemicals, disorders and their interactions. Using the entire Web of data is both unnecessary and computationally intensive, and the irrelevant portion can add to the noise which may negatively impact the performance of the application. This motivates the need to identify and extract relevant data for domain-specific applications from the Web of data. Therefore, this dissertation studies the problem of domain-specific knowledge extraction from the Web of data. The rapid growth of the Web of data takes place in three dimensions: 1) the number of knowledge graphs, 2) the size of the individual knowledge graph, and 3) the domain coverage. For example, the Linked Open Data (LOD), which is a collection of interlinked knowledge graphs on the Web, started with 12 datasets in 2007, and has evolved to more than 1100 datasets in 2017. DBpedia, which is a knowledge graph in the LOD, started with 3 million entities and 400 million relationships in 2012, and now has grown up to 38:3 million entities and 3 billion relationships. As we are interested in domain-specific applications and the domain of interest is already known, we propose to use the domain to restrict/reduce the other two dimensions from the Web of data. Reducing the first dimension requires to reduce the number of knowledge graphs by identifying relevant knowledge graphs to the domain. However, this still may result in large knowledge graphs such as DBpedia, Freebase, and YAGO that cover multiple domains including our domain of interest. Hence, it is required to reduce the size of the knowledge graphs by identifying the relevant portion of a large knowledge graph. This leads to two key research problems to address in this dissertation. (1) Can we identify the relevant knowledge graphs that represent a domain? and (2) Can we identify the relevant portion of a cross-domain knowledge graphs to represent the domain? A solution to the first problem requires automatically identifying the domain represented by each knowledge graph. This can be challenging for several reasons: 1) Knowledge graphs represent domains at different levels of abstractions and specificity, 2) a single knowledge graph can represent multiple domains (i.e., cross-domain knowledge graphs), and 3) the represented domains by knowledge graphs keep evolving. We propose to use existing crowd-sourced knowledge bases with their schema to automatically identify the domains and show its effectiveness in finding relevant knowledge graphs for specific domains. The challenge in addressing the second issue is the nature of the relationships connecting entities in these knowledge graphs. There are two types of relationships: 1) Hierarchical relationships, and 2) non-hierarchical relationships. While hierarchical relationships connect in-domain and out-of-domain entities using the same relationship type and hence represent uniform semantics, nonhierarchical relationships c...
Page Count
138
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2018
Copyright
Copyright 2018, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0000-0002-9602-3009
