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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.


The match process in GOMMA. GOMMA utilizes the sketched process to create ontology mappings. This process iteratively generates mappings between selected input ontologies and includes feedback from human experts.
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Figure 3: The match process in GOMMA. GOMMA utilizes the sketched process to create ontology mappings. This process iteratively generates mappings between selected input ontologies and includes feedback from human experts.

Mentions: Figure 3 shows a typical match process performed in GOMMA; the approach is inspired by the ontology matching system COMA++ [30] supporting the combined application of several matchers (match algorithms). The ontologies to be matched have at first to be integrated into the GOMMA repository. Match processing for two ontologies then entails the execution of several matchers that are selected from the library of supported matchers. Each matcher generates an intermediate mapping result represented by a matrix of computed similarity values (0 ≤ similarity ≤ 1) for pairs of concepts from the input ontologies. The similarity matrices are aggregated into a single similarity matrix (according to some selected combination strategy) in order to obtain a combined (still intermediate) match result. Furthermore, a filter step is applied (according to some selected filter strategy) to select the most likely correspondences for the mapping result. The determined ontology mapping can be stored within a mapping pool in the GOMMA repository. The match process may be applied iteratively to refine and improve an initially generated ontology mapping. Additionally, human experts can provide feedback to verify and correct computed mappings.


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)

The match process in GOMMA. GOMMA utilizes the sketched process to create ontology mappings. This process iteratively generates mappings between selected input ontologies and includes feedback from human experts.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: The match process in GOMMA. GOMMA utilizes the sketched process to create ontology mappings. This process iteratively generates mappings between selected input ontologies and includes feedback from human experts.
Mentions: Figure 3 shows a typical match process performed in GOMMA; the approach is inspired by the ontology matching system COMA++ [30] supporting the combined application of several matchers (match algorithms). The ontologies to be matched have at first to be integrated into the GOMMA repository. Match processing for two ontologies then entails the execution of several matchers that are selected from the library of supported matchers. Each matcher generates an intermediate mapping result represented by a matrix of computed similarity values (0 ≤ similarity ≤ 1) for pairs of concepts from the input ontologies. The similarity matrices are aggregated into a single similarity matrix (according to some selected combination strategy) in order to obtain a combined (still intermediate) match result. Furthermore, a filter step is applied (according to some selected filter strategy) to select the most likely correspondences for the mapping result. The determined ontology mapping can be stored within a mapping pool in the GOMMA repository. The match process may be applied iteratively to refine and improve an initially generated ontology mapping. Additionally, human experts can provide feedback to verify and correct computed mappings.

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.