How do Ontology Mappings Change in the Life Sciences?

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. We further investigate alternatives to predict or estimate the degree of future mapping changes based on previous ontology and mapping transitions.Comment: Keywords: mapping evolution, ontology matching, ontology evolutio

Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Increasingly, organizations are adopting ontologies to describe their large catalogues of items. These ontologies need to evolve regularly in response to changes in the domain and the emergence of new requirements. An important step of this process is the selection of candidate concepts to include in the new version of the ontology. This operation needs to take into account a variety of factors and in particular reconcile user requirements and application performance. Current ontology evolution methods focus either on ranking concepts according to their relevance or on preserving compatibility with existing applications. However, they do not take in consideration the impact of the ontology evolution process on the performance of computational tasks – e.g., in this work we focus on instance tagging, similarity computation, generation of recommendations, and data clustering. In this paper, we propose the Pragmatic Ontology Evolution (POE) framework, a novel approach for selecting from a group of candidates a set of concepts able to produce a new version of a given ontology that i) is consistent with the a set of user requirements (e.g., max number of concepts in the ontology), ii) is parametrised with respect to a number of dimensions (e.g., topological considerations), and iii) effectively supports relevant computational tasks. Our approach also supports users in navigating the space of possible solutions by showing how certain choices, such as limiting the number of concepts or privileging trendy concepts rather than historical ones, would reflect on the application performance. An evaluation of POE on the real-world scenario of the evolving Springer Nature taxonomy for editorial classification yielded excellent results, demonstrating a significant improvement over alternative approaches

An Evolution-based Approach for Assessing Ontology Mappings - A Case Study in the Life Sciences

Ontology matching has been widely studied. However, the resulting on-tology mappings can be rather unstable when the participating ontologies or util-ized secondary sources (e.g., instance sources, thesauri) evolve. We propose an evolution-based approach for assessing ontology mappings by annotating their cor-respondences by information about similarity values for past ontology versions. These annotations allow us to assess the stability of correspondences over time and they can thus be used to determine better and more robust ontology mappings. The approach is generic in that it can be applied independently from the utilized match technique. We define different stability measures and show results of a first evaluation for the life science domain

GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <url>http://dbs.uni-leipzig.de/GOMMA</url>.</p> <p>Conclusions</p> <p>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.</p

Management von Ontologien in den Lebenswissenschaften

Die Bedeutung bzw. der praktische Nutzen von Ontologien zeigt sich insbesondere in den Lebenswissenschaften. Durch die gestiegene Akzeptanz und Anwendung von Ontologien stellt deren Management mehr und mehr ein wichtiges Problem dar. Aus diesen Grund werden die ständige Weiterentwicklung (Evolution) von Ontologien und die starke Heterogenität bezüglich ihrer Formate in diesem Beitrag näher betrachtet. Im speziellen versucht ein erster Ansatz verschiedene Ontologien über eine Middleware in Gridumgebungen zu integrieren, um Gridnutzern bzw. Anwendungen im Grid einen einheitlichen und einfachen Zugang zu Ontologieinformationen zu ermöglichen. Eine weitere Untersuchung beschäftigt sich primär mit der Evolution von Ontologien. Auf Basis eines Frameworks wurde eine quantitative Analyse der Evolution von 16 aktuell verfügbaren, biomedizinischen Ontologien durchgeführt

XML-based approaches for the integration of heterogeneous bio-molecular data

Background: The today's public database infrastructure spans a very large collection of heterogeneous biological data, opening new opportunities for molecular biology, bio-medical and bioinformatics research, but raising also new problems for their integration and computational processing. Results: In this paper we survey the most interesting and novel approaches for the representation, integration and management of different kinds of biological data by exploiting XML and the related recommendations and approaches. Moreover, we present new and interesting cutting edge approaches for the appropriate management of heterogeneous biological data represented through XML. Conclusion: XML has succeeded in the integration of heterogeneous biomolecular information, and has established itself as the syntactic glue for biological data sources. Nevertheless, a large variety of XML-based data formats have been proposed, thus resulting in a difficult effective integration of bioinformatics data schemes. The adoption of a few semantic-rich standard formats is urgent to achieve a seamless integration of the current biological resources. </p

Analyse der Evolution von Mappings anhand ausgewählter Matchprobleme in den Lebenswissenschaften

Heute werden gleiche Themen durch verschiedene Nutzer bearbeitet, deshalb entstehen unterschiedliche Darstellungen desselben Sachverhalts. Um eine einheitliche Repräsentation von Daten zu ermöglichen, werden Ontologien verwendet. Ontologien stellen einen strukturierten gerichteten Graphen dar, dessen Knoten und Kanten jeweils die Konzepte und Beziehungen innerhalb einer Domäne repräsentieren. In den Lebenswissenschaften existieren zahlreiche große Ontologien wie z.B. CHEBI, NCI Thesaurus (The National Cancer Institute Thesaurus), GO (Gene Ontology). Da die Ontologien weiterentwickelt und wiederverwendet werden sollen, ist es wichtig, eine einheitliche und verständliche Syntax zu verwenden