He Tan:
Aligning Biomedical Ontologies.
Abstract
The amount of biomedical information that is disseminated over
the Web increases every day. This rich resource is used to find solutions
to challenges across the life sciences. The Semantic Web for life sciences
shows promise for effectively and efficiently locating, integrating,
querying and inferring related information that is needed in daily
biomedical research. One of the key technologies in the Semantic Web is
ontologies, which furnish the semantics of the Semantic Web. A large
number of biomedical ontologies have been developed. Many of these
ontologies contain overlapping information, but it is unlikely that
eventually there will be one single set of standard ontologies to which
everyone will conform. Therefore, applications often need to deal with
multiple overlapping ontologies, but the heterogeneity of ontologies
hampers interoperability between different ontologies. Aligning
ontologies, i.e. identifying relationships between different ontologies,
aims to overcome this problem. A number of ontology alignment systems have
been developed. In these systems various techniques and ideas have been
proposed to facilitate identification of alignments between
ontologies. However, there still is a range of issues to be addressed when
we have alignment problems at hand. The work in this thesis contributes to
three different aspects of identification of high quality alignments: 1)
Ontology alignment strategies and systems. We surveyed the existing
ontology alignment systems, and proposed a general ontology alignment
framework. Most existing systems can be seen as instantiations of the
framework. Also, we developed a system for aligning biomedical ontologies
(SAMBO) according to this framework. We implemented various alignment
strategies in the system. 2) Evaluation of ontology alignment
strategies. We developed and implemented the KitAMO framework for
comparative evaluation of different alignment strategies, and we evaluated
different alignment strategies using the implementation. 3) Recommending
optimal alignment strategies for different applications. We proposed a
method for making recommendations.
URL:
http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2007-076
@phdthesis{REWERSE-RP-2007-076, author = {He Tan}, title = {Aligning Biomedical Ontologies}, school = {Institute of Computer Science, LMU, Munich}, year = {2007}, note = {PhD Thesis, Linköping University, Department of Computer and Information Science, July 2007}, type = {{Dissertation/Ph.D. thesis}}, url = {http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2007-076} }