4 research outputs found
Developing Ontological Background Knowledge for Biomedicine
Biomedicine is an impressively fast developing, interdisciplinary field of
research. To control the growing volumes of biomedical data, ontologies are
increasingly used as common organization structures. Biomedical ontologies
describe domain knowledge in a formal, computationally accessible way. They
serve as controlled vocabularies and background knowledge in applications
dealing with the integration, analysis and retrieval of heterogeneous types
of data. The development of biomedical ontologies, however, is hampered by
specific challenges. They include the lack of quality standards, resulting
in very heterogeneous resources, and the decentralized development of
biomedical ontologies, causing the increasing fragmentation of domain
knowledge across them.
In the first part of this thesis, a life cycle model for biomedical
ontologies is developed, which is intended to cope with these challenges.
It comprises the stages "requirements analysis", "design and
implementation", "evaluation", "documentation and release" and
"maintenance". For each stage, associated subtasks and activities are
specified. To promote quality standards for biomedical ontology
development, an emphasis is set on the evaluation stage. As part of it,
comprehensive evaluation procedures are specified, which allow to assess
the quality of ontologies on various levels. To tackle the issue of
knowledge fragmentation, the life cycle model is extended to also cover
ontology alignments. Ontology alignments specify mappings between related
elements of different ontologies. By making potential overlaps and
similarities between ontologies explicit, they support the integration of
ontologies and help reduce the fragmentation of knowledge.
In the second part of this thesis, the life cycle model for biomedical
ontologies and alignments is validated by means of five case studies. As a
result, they confirm that the model is effective. Four of the case studies
demonstrate that it is able to support the development of useful new
ontologies and alignments. The latter facilitate novel natural language
processing and bioinformatics applications, and in one case constitute the
basis of a task of the "BioNLP shared task 2013", an international
challenge on biomedical information extraction. The fifth case study shows
that the presented evaluation procedures are an effective means to check
and improve the quality of ontology alignments. Hence, they support the
crucial task of quality assurance of alignments, which are themselves
increasingly used as reference standards in evaluations of automatic
ontology alignment systems. Both, the presented life cycle model and the
ontologies and alignments that have resulted from its validation improve
information and knowledge management in biomedicine and thus promote
biomedical research