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SerVCS: Serialization Agnostic Ontology Development in Distributed Settings

  • Lavdim HalilajEmail author
  • Irlán Grangel-González
  • Maria-Esther Vidal
  • Steffen Lohmann
  • Sören Auer
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 914)

Abstract

The development of domain-specific ontologies requires joint efforts among different groups of stakeholders, such as knowledge engineers and domain experts. During the development processes, ontology changes need to be tracked and propagated across developers. Version Control Systems (VCSs) collect metadata describing changes and allow for the synchronization of different versions of the same ontology. Commonly, VCSs follow optimistic approaches to enable the concurrent modification of ontology artifacts, as well as conflict detection and resolution. For conflict detection, VCSs usually apply techniques where files are compared line by line. However, ontology changes can be serialized in different ways during the development process. As a consequence, existing VCSs may detect a large number of false-positive conflicts, i.e., conflicts that do not result from ontology changes but from the fact that two ontology versions are differently serialized. We developed SerVCS in order to enhance VCSs to cope with different serializations of the same ontology, following the principle of prevention is better than cure. SerVCS resorts on unique ontology serializations and minimizes the number of false-positive conflicts. It is implemented on top of Git, utilizing tools such as Rapper and RDF-toolkit for syntax validation and unique serialization, respectively. We conducted an empirical evaluation to determine the conflict detection accuracy of SerVCS whenever simultaneous changes to an ontology are performed using different ontology editors. Experimental results suggest that SerVCS allows VCSs to conduct more effective synchronization processes by preventing false-positive conflicts.

Notes

Acknowledgements

This work has been supported by the European Union’s Horizon 2020 programme for the project BigDataEurope (grant no. 644564), and the German Federal Ministry of Education and Research (BMBF) for the projects Industrial Data Space (grant no. 01IS15054) and SDI-X (grant no. 01IS15035C).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lavdim Halilaj
    • 1
    • 2
    Email author
  • Irlán Grangel-González
    • 1
    • 2
  • Maria-Esther Vidal
    • 2
    • 3
    • 4
  • Steffen Lohmann
    • 2
  • Sören Auer
    • 3
    • 4
  1. 1.Smart Data Analytics (SDA)University of BonnBonnGermany
  2. 2.Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)Sankt AugustinGermany
  3. 3.TIB Leibniz Information Center for Science and TechnologyHannoverGermany
  4. 4.L3S Research CenterUniversity of HannoverHannoverGermany

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