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Generating Commonsense Ontologies With Answer Set Programming

ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2(2021)

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摘要
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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关键词
Knowledge Representation and Reasoning, Knowledge-based Systems, Ontologies
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