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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.


Comparative region analysis for top-level concepts of ChEBI. The figure shows the results of a region analysis for ChEBI top-level concepts. Red (green) categories evolved heavily (marginally) in the observation period and are thus unstable (stable). We analysed monthly released versions in 2009 (top) and 2010 (bottom).
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Figure 8: Comparative region analysis for top-level concepts of ChEBI. The figure shows the results of a region analysis for ChEBI top-level concepts. Red (green) categories evolved heavily (marginally) in the observation period and are thus unstable (stable). We analysed monthly released versions in 2009 (top) and 2010 (bottom).

Mentions: Figure 8 shows the region stability for the top-level categories of ChEBI (Chemical Entities of Biological Interest) [48]. We distinguish two periods, particular we investigated all released versions in 2009 (top) and all releases in 2010 (bottom). The root (ChEBI:24431 - chemical entity) representing the overall change intensity shows an increased instability in both periods. However, there are differences for the other categories. For instance, in 2010 a new sub ontology on "chemical substances" (ChEBI:59999) with high instability has been introduced. On the one hand, there are regions possessing less changes in 2010 compared to 2009, e.g., "group", "polyatomic entity" or "transition element molecular entity". On the other hand, work in some regions has become more intensive, e.g., "ion" or "group element atom".


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)

Comparative region analysis for top-level concepts of ChEBI. The figure shows the results of a region analysis for ChEBI top-level concepts. Red (green) categories evolved heavily (marginally) in the observation period and are thus unstable (stable). We analysed monthly released versions in 2009 (top) and 2010 (bottom).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Comparative region analysis for top-level concepts of ChEBI. The figure shows the results of a region analysis for ChEBI top-level concepts. Red (green) categories evolved heavily (marginally) in the observation period and are thus unstable (stable). We analysed monthly released versions in 2009 (top) and 2010 (bottom).
Mentions: Figure 8 shows the region stability for the top-level categories of ChEBI (Chemical Entities of Biological Interest) [48]. We distinguish two periods, particular we investigated all released versions in 2009 (top) and all releases in 2010 (bottom). The root (ChEBI:24431 - chemical entity) representing the overall change intensity shows an increased instability in both periods. However, there are differences for the other categories. For instance, in 2010 a new sub ontology on "chemical substances" (ChEBI:59999) with high instability has been introduced. On the one hand, there are regions possessing less changes in 2010 compared to 2009, e.g., "group", "polyatomic entity" or "transition element molecular entity". On the other hand, work in some regions has become more intensive, e.g., "ion" or "group element atom".

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.