Back to Search Start Over

Engineering uncertain time for its practical integration in ontologies.

Authors :
Siebra, Clauirton A.
Wac, Katarzyna
Source :
Knowledge-Based Systems. Sep2022, Vol. 251, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

Ontologies are commonly used as a strategy for knowledge representation. However, they are still presenting limitations to model domains that require broad forms of temporal reasoning. This study is part of the Onto-mQoL project and was motivated by the real need to extend static ontologies with diverse time concepts, relations and properties, which go beyond the commonly used Allen's Interval Algebra. Therefore, we use the n-ary relations as the basis for temporal structures, which minimally modify the original ontology, and extend these structures with a generic set of time concepts (moments and intervals), time concept properties (precise and uncertain), time relations (interval–interval, interval–moment, and moment–moment), and time relation properties (qualitative and quantitative). We divided the scientific contribution of this study into three parts. Firstly, we present the ontological temporal model (classes and properties) and how it is integrated into static ontologies. Secondly, we discuss the creation of axioms that give the semantics for precise temporal elements. Finally, as our main contribution, these ideas are extended with axioms for uncertain time. All these elements follow the Ontology Web Language (OWL) standards, so this proposal is still compatible with the main ontology editors and reasoners currently available. A case example demonstrates the use of this approach in the nutrition assessment domain. • Semantics definition of precise and uncertain time notions and relations. • Support for representations of different levels of uncertainty between events. • Compatibility with the current Semantic Web standards. • Knowledge engineering method to include time notion in static ontologies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09507051
Volume :
251
Database :
Academic Search Index
Journal :
Knowledge-Based Systems
Publication Type :
Academic Journal
Accession number :
158208371
Full Text :
https://doi.org/10.1016/j.knosys.2022.109152