Literature DB >> 22580476

COnto-Diff: generation of complex evolution mappings for life science ontologies.

Michael Hartung1, Anika Groß, Erhard Rahm.   

Abstract

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. We propose a novel approach COnto-Diff to determine an expressive and invertible diff evolution mapping between given versions of an ontology. Our approach first matches the ontology versions and determines an initial evolution mapping consisting of basic change operations (insert/update/delete). To semantically enrich the evolution mapping we adopt a rule-based approach to transform the basic change operations into a smaller set of more complex change operations, such as merge, split, or changes of entire subgraphs. The proposed algorithm is customizable in different ways to meet the requirements of diverse ontologies and application scenarios. We evaluate the proposed approach for large life science ontologies including the Gene Ontology and the NCI Thesaurus and compare it with PromptDiff. We further show how the Diff results can be used for version management and annotation migration in collaborative curation.
Copyright © 2012 Elsevier Inc. All rights reserved.

Mesh:

Year:  2012        PMID: 22580476     DOI: 10.1016/j.jbi.2012.04.009

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  9 in total

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3.  Combining rules, background knowledge and change patterns to maintain semantic annotations.

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4.  Characterizing semantic mappings adaptation via biomedical KOS evolution: a case study investigating SNOMED CT and ICD.

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5.  Region Evolution eXplorer - A tool for discovering evolution trends in ontology regions.

Authors:  Victor Christen; Michael Hartung; Anika Groß
Journal:  J Biomed Semantics       Date:  2015-06-01

6.  FEDRR: fast, exhaustive detection of redundant hierarchical relations for quality improvement of large biomedical ontologies.

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Review 7.  Evolution of biomedical ontologies and mappings: Overview of recent approaches.

Authors:  Anika Groß; Cédric Pruski; Erhard Rahm
Journal:  Comput Struct Biotechnol J       Date:  2016-08-26       Impact factor: 7.271

8.  Exploring biomedical ontology mappings with graph theory methods.

Authors:  Simon Kocbek; Jin-Dong Kim
Journal:  PeerJ       Date:  2017-03-02       Impact factor: 2.984

9.  Mining Relation Reversals in the Evolution of SNOMED CT Using MapReduce.

Authors:  Shiqiang Tao; Licong Cui; Wei Zhu; Mengmeng Sun; Olivier Bodenreider; Guo-Qiang Zhang
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2015-03-23
  9 in total

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