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Authors: Pietro Galliani ; Oliver Kutz and Roberto Confalonieri

Affiliation: Free University of Bozen-Bolzano, Faculty of Computer Science, 39100, Bozen-Bolzano and Italy

Keyword(s): Ontology Generation, Benchmarking, Testing.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Engineering ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Engineering ; Symbolic Systems

Abstract: As the sophistication of the tools available for manipulating ontologies increases, so does the need for novel and rich ontologies to use for purposes such as benchmarking, testing and validation. Ontology repositories are not ideally suited for this need, as the ontologies they contain are limited in number, may not generally have required properties (e.g., inconsistency), and may present unwelcome correlations between features. In order to better match this need, we hold that a highly tuneable, language-agnostic, theoretically principled tool for the automated generation of random ontologies is needed. In this position paper we describe how a probabilistic generative model (based on features obtained via the analysis of real ontologies) should be developed for use as the theoretical back-end for such an enterprise, and discuss the role of the DOL metalanguage in it.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Galliani, P.; Kutz, O. and Confalonieri, R. (2018). A Roadmap towards Tuneable Random Ontology Generation Via Probabilistic Generative Models. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KEOD; ISBN 978-989-758-330-8; ISSN 2184-3228, SciTePress, pages 351-357. DOI: 10.5220/0006961103510357

@conference{keod18,
author={Pietro Galliani. and Oliver Kutz. and Roberto Confalonieri.},
title={A Roadmap towards Tuneable Random Ontology Generation Via Probabilistic Generative Models},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KEOD},
year={2018},
pages={351-357},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006961103510357},
isbn={978-989-758-330-8},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KEOD
TI - A Roadmap towards Tuneable Random Ontology Generation Via Probabilistic Generative Models
SN - 978-989-758-330-8
IS - 2184-3228
AU - Galliani, P.
AU - Kutz, O.
AU - Confalonieri, R.
PY - 2018
SP - 351
EP - 357
DO - 10.5220/0006961103510357
PB - SciTePress