A hybrid reasoning mechanism for effective sensor selection for tasks

Name: A hybrid reasoning mechanism for effective sensor selection for tasks
Author: de Mel, G., Şensoy, Murat, Vasconcelos, W., Norman, T. J.

İsim	A hybrid reasoning mechanism for effective sensor selection for tasks
Yazar	de Mel, G., Şensoy, Murat, Vasconcelos, W., Norman, T. J.
Basım Tarihi:	2013-02
Basım Yeri	- Elsevier
Konu	Logic programming, Semantic web, Knowledge-based resource selection
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0952-1976
Kayıt Numarası	ccdfa52a-806c-4d25-8566-dac5dd5a9622
Lokasyon	Computer Science
Tarih	2013-02
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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

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A hybrid reasoning mechanism for effective sensor selection for tasks

Yazar de Mel, G., Şensoy, Murat, Vasconcelos, W., Norman, T. J.

Basım Tarihi 2013-02

Basım Yeri - Elsevier

Konu Logic programming, Semantic web, Knowledge-based resource selection

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0952-1976

Kayıt Numarası ccdfa52a-806c-4d25-8566-dac5dd5a9622

Lokasyon Computer Science

Tarih 2013-02

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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