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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.


Complex change operations in Mammalian Phenotype Ontology (left) and ChEBI (right). The diff for both ontologies was computed between the versions 2009-12 and 2010-12.
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Figure 10: Complex change operations in Mammalian Phenotype Ontology (left) and ChEBI (right). The diff for both ontologies was computed between the versions 2009-12 and 2010-12.

Mentions: A further GOMMA tool is COntoDiff (Complex Ontology Diff) [18] which allows users to find complex changes between ontology versions such as merges or splits of concepts. In contrast to many simple add or delete changes, such complex changes are more meaningful and allow users to better understand how ontologies have changed. COntoDiff uses the rule-based change detection mechanism of GOMMA's DIFF component. For illustration, Figure 10 shows the number of found complex changes between versions of the MammalianPhenotype ontology (MP) as well as ChEBI between 2009-12 and 2010-12. First, there is a high number of information extending operations such as addLeaf, split as well as a significant amount of subgraph additions in both ontologies. This corresponds to the growth rates already shown in Table 3. In ChEBI addLeaf is the dominating change operation (a factor of 10 more addLeaf changes compared to MP). The subgraph additions provide information about what topics have been newly introduced. For instance, in MP a large subgraph "increased tumor incidence" (MP:0010274) was added between 2009-12 and 2010-12 and comprises 25 new concepts. The subgraph contains information about specific tumor incidences such as increased muscle or eye tumor incidence. In ChEBI the largest added subgraph "organophosphate oxoanion" (ChEBI:58945) contained 341 concepts. It covers organic phosphoric acid derivative in which one or more oxygen atoms of the phosphate group(s) has been deprotonated. However, there is also a significant amount of other complex changes such as concept merges or moves of concepts. In MP the operation merge([MP:0000442, MP:0008525], MP:0008525) fuses the concepts "longitudinally short skull" into "decreased cranium height". In ChEBI "Ogawa trisaccharide 1" and "Ogawa trisaccharide 2" have been merged into CHEBI:52982. No concepts have been deleted in MP since it merely marks concepts as obsolete if they are no longer required or out-dated. In contrast five deletions of leaf concepts took place in ChEBI.


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)

Complex change operations in Mammalian Phenotype Ontology (left) and ChEBI (right). The diff for both ontologies was computed between the versions 2009-12 and 2010-12.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Complex change operations in Mammalian Phenotype Ontology (left) and ChEBI (right). The diff for both ontologies was computed between the versions 2009-12 and 2010-12.
Mentions: A further GOMMA tool is COntoDiff (Complex Ontology Diff) [18] which allows users to find complex changes between ontology versions such as merges or splits of concepts. In contrast to many simple add or delete changes, such complex changes are more meaningful and allow users to better understand how ontologies have changed. COntoDiff uses the rule-based change detection mechanism of GOMMA's DIFF component. For illustration, Figure 10 shows the number of found complex changes between versions of the MammalianPhenotype ontology (MP) as well as ChEBI between 2009-12 and 2010-12. First, there is a high number of information extending operations such as addLeaf, split as well as a significant amount of subgraph additions in both ontologies. This corresponds to the growth rates already shown in Table 3. In ChEBI addLeaf is the dominating change operation (a factor of 10 more addLeaf changes compared to MP). The subgraph additions provide information about what topics have been newly introduced. For instance, in MP a large subgraph "increased tumor incidence" (MP:0010274) was added between 2009-12 and 2010-12 and comprises 25 new concepts. The subgraph contains information about specific tumor incidences such as increased muscle or eye tumor incidence. In ChEBI the largest added subgraph "organophosphate oxoanion" (ChEBI:58945) contained 341 concepts. It covers organic phosphoric acid derivative in which one or more oxygen atoms of the phosphate group(s) has been deprotonated. However, there is also a significant amount of other complex changes such as concept merges or moves of concepts. In MP the operation merge([MP:0000442, MP:0008525], MP:0008525) fuses the concepts "longitudinally short skull" into "decreased cranium height". In ChEBI "Ogawa trisaccharide 1" and "Ogawa trisaccharide 2" have been merged into CHEBI:52982. No concepts have been deleted in MP since it merely marks concepts as obsolete if they are no longer required or out-dated. In contrast five deletions of leaf concepts took place in ChEBI.

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.