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Associative classification, linguistic entity relationship extraction, and description-logic representation of biomedical knowledge applied to MEDLINE Open Access


Other title
multi-label classification
ordered tree inclusion
associative classification
deep linguistic parsing
entity relationship extraction
knowledge representation
Type of item
Degree grantor
University of Alberta
Author or creator
Rak, Rafal
Supervisor and department
Kurgan, Lukasz (Electrical and Computer Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Examining committee member and department
Dick, Scott (Electrical and Computer Engineering)
Miller, James (Electrical and Computer Engineering, Committee Chair)
Musilek, Petr (Electrical and Computer Engineering)
Dumontier, Michel (Department of Biology, Carleton University, Ottawa, ON)
Zaiane, Osmar (Computing Science)
Department of Electrical and Computer Engineering

Date accepted
Graduation date
Doctor of Philosophy
Degree level
MEDLINE, a large and constantly increasing collection of biomedical article references, has been the source of numerous investigations related to textual information retrieval and knowledge capture, including article categorization, bibliometric analysis, semantic query answering, and biological concept recognition and relationship extraction. This dissertation discusses the design and development of novel methods that contribute to the tasks of document categorization and relationship extraction. The two investigations result in a fast tool for building descriptive models capable of categorizing documents to multiple labels and a highly effective method able to extract broad range of relationships between entities embedded in text. Additionally, an application that aims at representing the extracted knowledge in a strictly defined but highly expressive structure of ontology is presented. The classification of documents is based on an idea of building association rules that consist of frequent patterns of words appearing in documents and classes these patterns are likely to be assigned to. The process of building the models is based on a tree enumeration technique and dataset projection. The resulting algorithm offers two different tree traversing strategies, breadth-first and depth-first. The classification scenario involves the use of two alternative thresholding strategies based on either the document-independent confidence of the rules or a similarity measure between a rule and a document. The presented classification tool is shown to perform faster than other methods and is the first associative-classification solution to incorporate multiple classes and the information about recurrence of words in documents. The extraction of relations between entities embedded in text involves the utilization of the output of a constituent parser and a set of manually developed tree-like patterns. Both serve as the input of a novel algorithm that solves the newly formulated problem of constrained constituent tree inclusion with regular expression matching. The proposed relation extraction method is demonstrated to be parser-independent and outperforms in terms of effectiveness dependency-parser-based and machine-learning-based solutions. The extracted knowledge is further embedded in an existing ontology, which together with the structure-driven modification of the ontology results in a comprehensible, inference-consistent knowledge base constituting a tangible representation of knowledge and a potential component of applications such as semantically enhanced query answering systems.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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