Generating Commonsense Ontologies with Answer Set Programming

Stefan Jakob, Alexander Jahl, Harun Baraki, Kurt Geihs

Abstract

The use of commonsense knowledge is essential for the interaction of humans and robots in a smart environment. This need arises from the way humans naturally communicate with each other, in which most details are usually omitted due to common background knowledge. To enable such communication with a robot, it has to be equipped with a commonsense knowledge representation that supports reasoning. Ontologies could be a suitable approach. However, current ontology frameworks lack dynamic adaptability, are monotonous, are missing negation as failure, and are not designed for huge amounts of data. This paper presents a new way to model ontologies based on a non-monotonic reasoning formalism. Our ontology modelling framework, called ARRANGE, allows for the automatic integration of graph-based knowledge sources to generate ontologies and provides corresponding tools. The presented experiments show the applicability of the generated ontologies and the performance of the ontology generation, the ontology reasoning, and the query resolution.

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Paper Citation


in Harvard Style

Jakob S., Jahl A., Baraki H. and Geihs K. (2021). Generating Commonsense Ontologies with Answer Set Programming.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 538-545. DOI: 10.5220/0010191905380545


in Bibtex Style

@conference{icaart21,
author={Stefan Jakob and Alexander Jahl and Harun Baraki and Kurt Geihs},
title={Generating Commonsense Ontologies with Answer Set Programming},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={538-545},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010191905380545},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Generating Commonsense Ontologies with Answer Set Programming
SN - 978-989-758-484-8
AU - Jakob S.
AU - Jahl A.
AU - Baraki H.
AU - Geihs K.
PY - 2021
SP - 538
EP - 545
DO - 10.5220/0010191905380545