A Roadmap towards Tuneable Random Ontology Generation Via Probabilistic Generative Models

Pietro Galliani, Oliver Kutz, Roberto Confalonieri

2018

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.

Paper Citation

in Harvard Style

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) - Volume 2: KEOD; ISBN 978-989-758-330-8, SciTePress, pages 351-357. DOI: 10.5220/0006961103510357

in Bibtex Style

@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) - Volume 2: KEOD},
year={2018},
pages={351-357},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006961103510357},
isbn={978-989-758-330-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 2: KEOD
TI - A Roadmap towards Tuneable Random Ontology Generation Via Probabilistic Generative Models
SN - 978-989-758-330-8
AU - Galliani P.
AU - Kutz O.
AU - Confalonieri R.
PY - 2018
SP - 351
EP - 357
DO - 10.5220/0006961103510357
PB - SciTePress