Article

Universal OWL axiom enrichment for large knowledge bases

Authors:

Lorenz Bühmann,

Jens LehmannAuthors Info & Claims

EKAW'12: Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management

Pages 57 - 71

https://doi.org/10.1007/978-3-642-33876-2_8

Published: 08 October 2012 Publication History

Abstract

The Semantic Web has seen a rise in the availability and usage of knowledge bases over the past years, in particular in the Linked Open Data initiative. Despite this growth, there is still a lack of knowledge bases that consist of high quality schema information and instance data adhering to this schema. Several knowledge bases only consist of schema information, while others are, to a large extent, a mere collection of facts without a clear structure. The combination of rich schema and instance data would allow powerful reasoning, consistency checking, and improved querying possibilities as well as provide more generic ways to interact with the underlying data. In this article, we present a light-weight method to enrich knowledge bases accessible via SPARQL endpoints with almost all types of OWL 2 axioms. This allows to semi-automatically create schemata, which we evaluate and discuss using DBpedia.

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Melo APaulheim H(2020)Automatic detection of relation assertion errors and induction of relation constraintsSemantic Web10.3233/SW-20036911:5(801-830)Online publication date: 1-Jan-2020
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https://dl.acm.org/doi/10.14778/3407790.3407806
Malchiodi Dda Costa Pereira CTettamanzi A(2020)Classifying Candidate Axioms via Dimensionality Reduction TechniquesModeling Decisions for Artificial Intelligence10.1007/978-3-030-57524-3_15(179-191)Online publication date: 2-Sep-2020
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Information & Contributors

Information

Published In

cover image Guide Proceedings

EKAW'12: Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management

October 2012

449 pages

ISBN:9783642338755

Editors:
Annette Teije
Vrije Universiteit, Amsterdam, The Netherlands
,
Johanna Völker
Institute of Computer Science and Business Informatics, University of Mannheim, Amsterdam, Germany
,
Siegfried Handschuh
Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland
,
Heiner Stuckenschmidt
Institute of Computer Science and Business Informatics, University of Mannheim, Amsterdam, Germany
,
Mathieu d'Acquin
Knowledge Media Institute, The Open University, Milton Keynes, UK

Sponsors

INES: Institute for Enterprise Systems
DERI
Open PHACTS: Open Pharmacological Space
NUIG: NUI Galway
LOD2: Creating Knowledge out of Interlinked Data, FP7 Research Project, National University of Ireland

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 October 2012

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Cited By

Melo APaulheim H(2020)Automatic detection of relation assertion errors and induction of relation constraintsSemantic Web10.3233/SW-20036911:5(801-830)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-200369
Bashardoost BMiller RLyons KNargesian F(2020)Knowledge translationProceedings of the VLDB Endowment10.14778/3407790.340780613:12(2018-2032)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3407790.3407806
Malchiodi Dda Costa Pereira CTettamanzi A(2020)Classifying Candidate Axioms via Dimensionality Reduction TechniquesModeling Decisions for Artificial Intelligence10.1007/978-3-030-57524-3_15(179-191)Online publication date: 2-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-57524-3_15
Yu RGadiraju UFetahu BLehmberg ORitze DDietze Sd’Amato CLawrynowicz ALehmann J(2019)KnowMore – knowledge base augmentation with structured web markupSemantic Web10.3233/SW-18030410:1(159-180)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/SW-180304
Abedini FMenhaj MKeyvanpour M(2019)An MLP-based representation of neural tensor networks for the RDF data modelsNeural Computing and Applications10.1007/s00521-017-3053-131:2(1135-1144)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00521-017-3053-1
Nguyen TTettamanzi A(2019)Learning Class Disjointness Axioms Using Grammatical EvolutionGenetic Programming10.1007/978-3-030-16670-0_18(278-294)Online publication date: 24-Apr-2019
https://dl.acm.org/doi/10.1007/978-3-030-16670-0_18
Bühmann LLehmann JWestphal PBin SChampin PGandon FMédini LLalmas MIpeirotis P(2018)DL-Learner Structured Machine Learning on Semantic Web DataCompanion Proceedings of the The Web Conference 201810.1145/3184558.3186235(467-471)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3186235
Malchiodi DTettamanzi AHaddad HWainwright RChbeir R(2018)Predicting the possibilistic score of OWL axioms through modified support vector clusteringProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167345(1984-1991)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167345
Saïs FPruski CDa Silveira M(2017)Inferring the evolution of ontology axioms from RDF data dynamicsProceedings of the 9th Knowledge Capture Conference10.1145/3148011.3154472(1-4)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.1145/3148011.3154472
Tettamanzi AZucker CGandon FBarker KGómez-Pérez J(2015)Dynamically Time-Capped Possibilistic Testing of SubClassOf Axioms Against RDF Data to Enrich SchemasProceedings of the 8th International Conference on Knowledge Capture10.1145/2815833.2815835(1-8)Online publication date: 7-Oct-2015
https://dl.acm.org/doi/10.1145/2815833.2815835
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