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GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution.

Kirsten T, Gross A, Hartung M, Rahm E - J Biomed Semantics (2011)

Bottom Line: Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.We introduce the component-based infrastructure and show analysis results for selected components and life science applications.GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany. tkirsten@izbi.uni-leipzig.de.

ABSTRACT

Background: Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.

Results: We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA.

Conclusions: GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

No MeSH data available.


Long-term region analysis for top-level concepts of NCI Thesaurus. Tracking of average costs for sample regions in NCI Thesaurus between 2004 and 2009.
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Figure 7: Long-term region analysis for top-level concepts of NCI Thesaurus. Tracking of average costs for sample regions in NCI Thesaurus between 2004 and 2009.

Mentions: The region detection algorithm allows tracking the stability of ontology regions over time. Figure 7 displays the development of average change costs in NCI Thesaurus between 2004 and 2009 for three selected main categories. The computation used a sliding window of 'half year' size (window step: 1 month). This trend analysis exposed different evolution patterns. "Drugs and Chemicals" strongly evolved (red line) and were thus unstable over the whole period. Such regions represent very active research fields and may be modified in the future again. The "Organism" region (orange line) had periods of high and low stability. The periods of high instability may be influenced by new research findings or restructuring decision in the ontology consortium. Finally, the "Anatomic structure or substance" region (green line) remained more or less stable since 2007, indicating that the development of the anatomy part of the NCI Thesaurus may be almost finished as it covers accepted and standardized knowledge.


GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution.

Kirsten T, Gross A, Hartung M, Rahm E - J Biomed Semantics (2011)

Long-term region analysis for top-level concepts of NCI Thesaurus. Tracking of average costs for sample regions in NCI Thesaurus between 2004 and 2009.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3198872&req=5

Figure 7: Long-term region analysis for top-level concepts of NCI Thesaurus. Tracking of average costs for sample regions in NCI Thesaurus between 2004 and 2009.
Mentions: The region detection algorithm allows tracking the stability of ontology regions over time. Figure 7 displays the development of average change costs in NCI Thesaurus between 2004 and 2009 for three selected main categories. The computation used a sliding window of 'half year' size (window step: 1 month). This trend analysis exposed different evolution patterns. "Drugs and Chemicals" strongly evolved (red line) and were thus unstable over the whole period. Such regions represent very active research fields and may be modified in the future again. The "Organism" region (orange line) had periods of high and low stability. The periods of high instability may be influenced by new research findings or restructuring decision in the ontology consortium. Finally, the "Anatomic structure or substance" region (green line) remained more or less stable since 2007, indicating that the development of the anatomy part of the NCI Thesaurus may be almost finished as it covers accepted and standardized knowledge.

Bottom Line: Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.We introduce the component-based infrastructure and show analysis results for selected components and life science applications.GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany. tkirsten@izbi.uni-leipzig.de.

ABSTRACT

Background: Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.

Results: We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA.

Conclusions: GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

No MeSH data available.