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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.


Evolution statistics in OnEX. The figure shows selected use cases of the web-based system Ontology Evolution Explorer (http://www.izbi.de/onex). The overview shows statistics for all ontologies currently integrated in OnEX. Tracking changes, the list of changed concepts and quantitative difference statistics are shown for the Mammalian Phenotype Ontology.
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Figure 5: Evolution statistics in OnEX. The figure shows selected use cases of the web-based system Ontology Evolution Explorer (http://www.izbi.de/onex). The overview shows statistics for all ontologies currently integrated in OnEX. Tracking changes, the list of changed concepts and quantitative difference statistics are shown for the Mammalian Phenotype Ontology.

Mentions: The GOMMA-based Ontology Evolution Explorer (OnEX) [17] available at http://www.izbi.de/onex provides change statistics for numerous ontologies and supports interactive exploration of their evolution histories. Currently, OnEX covers more than 780 versions of 16 life science ontologies dating back to 2002. The evolution statistics are available at the level of entire ontologies as well as at the concept and attribute levels. Figure 5 shows the OnEX user interface with exemplary data on the evolution of the Mammalian Phenotype ontology [47]. On the ontology level, details are provided about the first and last available version, the total number of versions, and the number of their concepts and relationships. For a selected ontology, the user can explore how many and which concepts and attributes changed in which way (add, delete, etc.) between succeeding versions (bottom left in Figure 5). Additionally, OnEX allows searching for concepts by specified keywords and lists their changes at the attribute level (right part of Figure 5). Ontology users such as curators or researchers, can thus track all changes in detail, e.g., according to the name, definition or relationships of selected concepts. Common ontology browsers do not provide such detailed information about conceptual and structural ontology changes. Curators may easier review their own changes and thus prepare future revisions, e.g., to correct erroneous changes. For the introduced application scenario, such information may help to explain changes as we will see in the analysis results.


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)

Evolution statistics in OnEX. The figure shows selected use cases of the web-based system Ontology Evolution Explorer (http://www.izbi.de/onex). The overview shows statistics for all ontologies currently integrated in OnEX. Tracking changes, the list of changed concepts and quantitative difference statistics are shown for the Mammalian Phenotype Ontology.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Evolution statistics in OnEX. The figure shows selected use cases of the web-based system Ontology Evolution Explorer (http://www.izbi.de/onex). The overview shows statistics for all ontologies currently integrated in OnEX. Tracking changes, the list of changed concepts and quantitative difference statistics are shown for the Mammalian Phenotype Ontology.
Mentions: The GOMMA-based Ontology Evolution Explorer (OnEX) [17] available at http://www.izbi.de/onex provides change statistics for numerous ontologies and supports interactive exploration of their evolution histories. Currently, OnEX covers more than 780 versions of 16 life science ontologies dating back to 2002. The evolution statistics are available at the level of entire ontologies as well as at the concept and attribute levels. Figure 5 shows the OnEX user interface with exemplary data on the evolution of the Mammalian Phenotype ontology [47]. On the ontology level, details are provided about the first and last available version, the total number of versions, and the number of their concepts and relationships. For a selected ontology, the user can explore how many and which concepts and attributes changed in which way (add, delete, etc.) between succeeding versions (bottom left in Figure 5). Additionally, OnEX allows searching for concepts by specified keywords and lists their changes at the attribute level (right part of Figure 5). Ontology users such as curators or researchers, can thus track all changes in detail, e.g., according to the name, definition or relationships of selected concepts. Common ontology browsers do not provide such detailed information about conceptual and structural ontology changes. Curators may easier review their own changes and thus prepare future revisions, e.g., to correct erroneous changes. For the introduced application scenario, such information may help to explain changes as we will see in the analysis results.

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.