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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.


Versions of ontologies and entity sources and mappings among them. The figure shows the versioning of ontologies and entity sources and their interrelation using ontology, annotation, and evolution mappings.
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Figure 1: Versions of ontologies and entity sources and mappings among them. The figure shows the versioning of ontologies and entity sources and their interrelation using ontology, annotation, and evolution mappings.

Mentions: Furthermore, GOMMA supports the management of different kinds of ontology-based mappings describing how ontologies are related to other ontologies or how they relate to biomedical entities such as gene or protein descriptions. Figure 1 illustrates how ontologies, entity sources as well as their versions are related in the life science domain leading to different types of mappings among them. Entity sources like the genome source Ensembl [13] or Swiss-Prot [14] use ontology concepts to uniformly describe or annotate their objects (e.g., genes). Such a set of links between an ontology and entity source forms a so-called annotation mapping. Moreover, ontology mappings contain correspondences between overlapping ontologies that interrelate semantically equivalent or related ontology concepts. Changes to ontologies and entity sources are reflected in regularly released new versions; changes between two succeeding versions can be captured by so-called evolution mappings. Changes of ontologies and entity sources may make it necessary to adapt the dependent ontology and annotation mappings accordingly. GOMMA helps to address these problems by determining evolutionary changes of ontologies and their mappings as well as supporting different kinds of evolution analysis.


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)

Versions of ontologies and entity sources and mappings among them. The figure shows the versioning of ontologies and entity sources and their interrelation using ontology, annotation, and evolution mappings.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Versions of ontologies and entity sources and mappings among them. The figure shows the versioning of ontologies and entity sources and their interrelation using ontology, annotation, and evolution mappings.
Mentions: Furthermore, GOMMA supports the management of different kinds of ontology-based mappings describing how ontologies are related to other ontologies or how they relate to biomedical entities such as gene or protein descriptions. Figure 1 illustrates how ontologies, entity sources as well as their versions are related in the life science domain leading to different types of mappings among them. Entity sources like the genome source Ensembl [13] or Swiss-Prot [14] use ontology concepts to uniformly describe or annotate their objects (e.g., genes). Such a set of links between an ontology and entity source forms a so-called annotation mapping. Moreover, ontology mappings contain correspondences between overlapping ontologies that interrelate semantically equivalent or related ontology concepts. Changes to ontologies and entity sources are reflected in regularly released new versions; changes between two succeeding versions can be captured by so-called evolution mappings. Changes of ontologies and entity sources may make it necessary to adapt the dependent ontology and annotation mappings accordingly. GOMMA helps to address these problems by determining evolutionary changes of ontologies and their mappings as well as supporting different kinds of evolution analysis.

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.