research-article

MontoloStats - Ontology Modeling Statistics

Authors:

Ben De Meester,

Anastasia Dimou,

Ruben VerborghAuthors Info & Claims

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

Pages 69 - 76

https://doi.org/10.1145/3360901.3364433

Published: 23 September 2019 Publication History

Abstract

Within ontology engineering concepts are modeled as classes and relationships, and restrictions as axioms. Reusing ontologies requires assessing if existing ontologies are suited for an application scenario. Different scenarios not only influence concept modeling, but also the use of different restriction types, such as subclass relationships or disjointness between concepts. However, metadata about the use of such restriction types is currently unavailable, preventing accurate assessments for reuse. We created the RDF Data Cube-based dataset MontoloStats, which contains restriction use statistics for 660 LOV and 565 BioPortal ontologies. We analyze the dataset and discuss the findings and their implications for ontology reuse. The MontoloStats dataset reveals that 94% of LOV and 95% of BioPortal ontologies use RDFS-based restriction types, 49% of LOV and 52% of BioPortal ontologies use at least one OWL-based restriction type, and different literal value-related restriction types are not or barely used. Our dataset provides modeling insights, beneficial for ontology reuse to discover and compare reuse candidates, but can also be the basis of new research that investigates novel ontology engineering methodologies with respect to restrictions definition.

References

[1]

Dörthe Arndt, Ben De Meester, Anastasia Dimou, Ruben Verborgh, and Erik Mannens. 2017. Using Rule Based Reasoning for RDF Validation. In RuleML

[2]

RR .

[3]

Sören Auer, Jan Demter, Michael Martin, and Jens Lehmann. 2012. LODStats -- An Extensible Framework for High-Performance Dataset Analytics. In Knowledge engineering and management . Springer.

[4]

Niyati Baliyan and Sandeep Kumar. 2016. Towards measurement of structural complexity for ontologies. International Journal of Web Engineering and Technology (2016).

[5]

Antonio De Nicola and Michele Missikoff. 2016. A lightweight methodology for rapid ontology engineering. Commun. ACM (2016).

[6]

Antonio De Nicola, Michele Missikoff, and Roberto Navigli. 2009. A software engineering approach to ontology building. Information systems (2009).

[7]

Nicola Guarino and Pierdaniele Giaretta. 1995. Ontologies and knowledge bases: Towards a terminological clarification. In Towards very Large Knowledge bases: Knowledge Building and Knowledge sharing . IOS Press.

[8]

Thomas Hartmann. 2016. Validation Framework for RDF-based Constraint Languages . Ph.D. Dissertation. Karlsruher Institut für Technologie (KIT).

[9]

Dimitris Kontokostas, Patrick Westphal, Sören Auer, Sebastian Hellmann, Jens Lehmann, Roland Cornelissen, and Amrapali Zaveri. 2014. Test-driven evaluation of linked data quality. In 23textsuperscriptrd international conference on World Wide Web. ACM.

[10]

Jose Emilio Labra Gayo, Eric Prud'hommeaux, Iovka Boneva, and Dimitris Kontokostas. 2017. Validating RDF Data. Morgan & Claypool Publishers LLC .

[11]

Andreas Langegger and Wolfram Woss. 2009. RDFStats - An Extensible RDF Statistics Generator and Library. In Proceedings of the 20th International Workshop on Database and Expert Systems Applications . IEEE Computer Society.

Digital Library

[12]

Nandana Mihindukulasooriya, Poveda-Villalón, María-Castro, Raúl, and Asunción Gómez-Pérez. 2015. Loupe-An Online Tool for Inspecting Datasets in the Linked Data Cloud. In International Semantic Web Conference (Posters & Demos) .

[13]

M Musen, N Shah, N Noy, Benjamin Dai, Michael Dorf, N Griffith, JD Buntrock, Clement Jonquet, MJ Montegut, and Daniel L Rubin. 2008. BioPortal: ontologies and data resources with the click of a mouse. In AMIA Annu Symp Proc, Vol. 6. 1223--1224.

[14]

Mark A. Musen. 2015. The Protégé Project: A Look Back and a Look Forward. AI Matters (2015).

[15]

James A Overton, Heiko Dietze, Shahim Essaid, David Osumi-Sutherland, and Christopher J Mungall. 2015. ROBOT: A command-line tool for ontology development. In ICBO .

[16]

Elena Simperl. 2009. Reusing ontologies on the Semantic Web: A feasibility study. Data and Knowledge Engineering (2009).

[17]

Elena Simperl. 2010. Guidelines for reusing ontologies on the semantic web. International Journal of Semantic Computing (2010).

[18]

Elena Simperl and Christoph Tempich. 2006. Ontology engineering: a reality check. In International Conference "On the Move to Meaningful Internet Systems". Springer.

Digital Library

[19]

Elena Simperl, Christoph Tempich, and York Sure. 2006. ONTOCOM: a cost estimation model for ontology engineering. In The Semantic Web - ISWC 2006 . Springer Berlin Heidelberg, Berlin, Heidelberg.

[20]

Dominik Tomaszuk. 2018. Inference rules for OWL-P in N3Logic. In Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems (Annals of Computer Science and Information Systems). PTI.

[21]

Pierre-Yves Vandenbussche, Ghislain A Atemezing, Mar'ia Poveda-Villalón, and Bernard Vatant. 2017. Linked Open Vocabularies (LOV): a gateway to reusable semantic vocabularies on the Web. Semantic Web (2017).

Cited By

Svátek VZamazal ONguyen VIvánek JKľuka JVacura M(2023)Focused categorization power of ontologies: General framework and study on simple existential concept expressionsSemantic Web10.3233/SW-23340114:6(1209-1253)Online publication date: 13-Dec-2023
https://doi.org/10.3233/SW-233401
Lieber SDe Meester BHeyvaert PBrückmann FWambacq RMannens EVerborgh RDimou A(2022)Visual notations for viewing RDF constraints with UnSHACLedSemantic Web10.3233/SW-21045013:5(757-792)Online publication date: 18-Aug-2022
https://doi.org/10.3233/SW-210450
Deepak GA. AA. SPriyadarshini S(2021)Ontology Modelling for Metallurgy as a Domain and Retrieval Using Particle Swarm OptimizationMachine Learning Approaches for Improvising Modern Learning Systems10.4018/978-1-7998-5009-0.ch011(272-301)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5009-0.ch011

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MontoloStats - Ontology Modeling Statistics

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Published In

cover image ACM Conferences

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

September 2019

281 pages

ISBN:9781450370080

DOI:10.1145/3360901

General Chairs:
Mayank Kejriwal
University of Southern California Information Sciences Institute, USA
,
Pedro Szekely
University of Southern California Information Sciences Institute, USA
,
Program Chair:
Raphaël Troncy
EURECOM, France

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

Publication History

Published: 23 September 2019

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K-CAP '19

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K-CAP '19: Knowledge Capture Conference

November 19 - 21, 2019

CA, Marina Del Rey, USA

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Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Svátek VZamazal ONguyen VIvánek JKľuka JVacura M(2023)Focused categorization power of ontologies: General framework and study on simple existential concept expressionsSemantic Web10.3233/SW-23340114:6(1209-1253)Online publication date: 13-Dec-2023
https://doi.org/10.3233/SW-233401
Lieber SDe Meester BHeyvaert PBrückmann FWambacq RMannens EVerborgh RDimou A(2022)Visual notations for viewing RDF constraints with UnSHACLedSemantic Web10.3233/SW-21045013:5(757-792)Online publication date: 18-Aug-2022
https://doi.org/10.3233/SW-210450
Deepak GA. AA. SPriyadarshini S(2021)Ontology Modelling for Metallurgy as a Domain and Retrieval Using Particle Swarm OptimizationMachine Learning Approaches for Improvising Modern Learning Systems10.4018/978-1-7998-5009-0.ch011(272-301)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5009-0.ch011

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