He Tan, Lic:

Title:   Aligning and Merging Biomedical Ontologies

Abstract: Due to the explosion of the amount of biomedical data, knowledge and
tools that are often publicly available over the Web, a number of difficulties
are experienced by biomedical researchers. For instance, it is difficult to
find, retrieve and integrate information that is relevant to their research
tasks. Ontologies and the vision of a Semantic Web for life sciences alleviate
these difficulties.  
In recent years many biomedical ontologies have been developed and many of these
ontologies contain overlapping information.  To be able to use multiple
ontologies they have to be aligned or merged. A number of systems have been
developed for aligning and merging ontologies and various alignment strategies
are used in these systems. However, there are no general methods to support
building such tools, and there exist very few evaluations of these strategies.
In this thesis we give an overview of the existing systems. We propose a general
framework for aligning and merging ontologies. Most existing systems can be seen
as instantiations of this framework. Further, we develop SAMBO (System for
Aligning and Merging Biomedical Ontologies) according to this framework. We
implement different alignment strategies and their combinations, and evaluate
them in terms of quality and processing time within SAMBO. We also compare SAMBO
with two other systems. The work in this thesis is a first step towards a
general framework that can be used for comparative evaluations of alignment
strategies and their combinations.



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Courses Spring 2016


Last modified on March 2006 by Anne Moe