Author
de Mel, G., Şensoy, Murat, Vasconcelos, W., Norman, T. J.
Publication Date
2013-02
Publication Place
-
Elsevier
Subject
Logic programming, Semantic web, Knowledge-based resource selection
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0952-1976
Record ID
ccdfa52a-806c-4d25-8566-dac5dd5a9622
Library Location
Computer Science
Date
2013-02
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
In this paper, we present Ontological Logic Programming (OLP), a novel approach that combines logic programming with ontological reasoning. OLP enables the use of ontological terms (i.e., individuals, classes and properties) directly within logic programmes. The interpretation of these terms is delegated to an ontology reasoner during the interpretation of the programme. Unlike similar approaches, OLP makes use of the full capacity of both ontological reasoning and logic programming. We evaluate the computational properties of OLP in different settings and show that its performance can be significantly improved using caching mechanisms. We then introduce a comprehensive sensor-task selection solution based on OLP and discuss the benefits one can obtain by using OLP. The solution is based on a set of interlinking ontologies that capture the crucial domain knowledge of sensor networks. We then make use of OLP to create and manage complex concepts in the domain as well as to implement effective resource-task assignment algorithms, which compute appropriate resources for tasks such that they sufficiently cover the tasks needs. We compare the advantages of OLP with a knowledge-based set-covering mechanism for resource-task selection.
DOI
10.1016/j.engappai.2012.12.003
Cilt
26
