Research paper

GOMMA: A Component-based Infrastructure for managing and analyzing Life Science Ontologies and their Evolution

Authors: 
Kirsten, T.; Gross, A.; Hartung, M.; Rahm, E.
Year: 
2011
Venue: 
Journal of Biomedical Semantics 2011, 2:6

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

E ffective Mapping Composition for Biomedical Ontologies

Authors: 
Hartung, M.; Gross, A.; Kirsten, T.; Rahm, E.
Year: 
2012
Venue: 
Semantic Interoperability in Medical Informatics @ ESWC 2012

There is an increasing need to interconnect biomedical ontologies. We investigate a simple but promising approach to generate mappings between ontologies by reusing and composing existing mappings across intermediate ontologies. Such an approach is especially promising for highly interconnected ontologies such as in the life science domain. There may be many ontologies that can be used for composition so that the problem arises to fi nd the most suitable ones providing the best results.

How do Ontology Mappings Change in the Life Sciences?

Authors: 
Gross, A.; Hartung, M.; Thor, A.; Rahm, E.
Year: 
2012

Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life sciences. We therefore analyze how mappings between popular life science ontologies evolve for different match algorithms. We also evaluate which semantic ontology changes primarily affect the mappings.

COnto-Diff : Generation of Complex Evolution Mappings for Life Science Ontologies

Authors: 
Hartung, M.; Gross, A.; Rahm, E.
Year: 
2012
Venue: 
Journal of Biomedical Informatics

Life science ontologies evolve frequently to meet new requirements or to better reflect the current domain knowledge. The development and adaptation of large and complex ontologies is typically performed collaboratively by several curators. To effectively manage the evolution of ontologies it is essential to identify the difference (Diff) between ontology versions. Such a Diff supports the synchronization of changes in collaborative curation, the adaptation of dependent data such as annotations, and ontology version management.

Mapping Composition for Matching Large Life Science Ontologies

Authors: 
Gross, A.; Hartung, M.; Kirsten, T.; Rahm, E.
Year: 
2011
Venue: 
2nd International Conference on Biomedical Ontology (ICBO)

There is an increasing need to interrelate different life science ontologies in order to facilitate data integration or semantic data analysis. Ontology matching aims at a largely automatic generation of mappings between ontologies mostly by calculating the linguistic and structural similarity of their concepts. In this paper we investigate an indirect computation of ontology mappings that composes and thus reuses previously determined ontology mappings that involve intermediate ontologies. The composition approach promises a fast computation of new mappings with reduced manual effort.

On Matching Large Life Science Ontologies in Parallel

Authors: 
Gross, A; Hartung, M; Kirsten, T; Rahm, E
Year: 
2010
Venue: 
Data Integration in the Life Sciences (DILS)

Matching life science ontologies to determine ontology mappings has recently become an active field of research. The large size of existing ontologies and the application of complex match strategies for obtaining high quality mappings makes ontology matching a resource- and time-intensive process. To improve performance we investigate different approaches for parallel matching on multiple compute nodes. In particular, we consider inter-matcher and intra-matcher parallelism as well as the parallel execution of element- and structure-level matching.

Discovering Evolving Regions in Life Science Ontologies

Authors: 
Hartung, M; Gross, A; Kirsten, T; Rahm, E
Year: 
2010
Venue: 
Data Integration in the Life Sciences (DILS)

Ontologies are heavily used in life sciences and evolve continuously to incorporate new or changed insights. Often ontology changes affect only specific parts (regions) of ontologies making it valuable for ontology users and applications to know the heavily changed regions on the one hand and stable regions on the other hand. However, the size and complexity of life science ontologies renders manual approaches to localize changing or stable regions impossible. We therefore propose an approach to automatically discover evolving or stable ontology regions.

Scalable Architecture and Query Optimization for Transaction-time DBs with Evolving Schemas

Authors: 
Moon, Hyun J.; Curino, Carlo; Zaniolo, Carlo
Year: 
2010
Venue: 
SIGMOD

The problem of archiving and querying the history of a database is made more complex by the fact that, along with the database content, the database schema also evolves with time.

An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size

Authors: 
Seddiqui, Md. Hanif; Aono, Masaki
Year: 
2009

It has been a formidable task to achieve efficiency and scalability for the alignment between two massive, conceptually similar ontologies. Here we assume, an ontology is typically given in RDF (Resource Description Framework) or OWL (Web Ontology Language) and can be represented by a directed graph. A straightforward approach to the alignment of two ontologies entails an O(N2) computation by comparing every combination of pairs of nodes from given two ontologies, where N denotes the average number of nodes in each ontology.

Syndicate content