A hybrid reasoning mechanism for effective sensor selection for tasks

Geeth De Mel*, Murat Sensoy, Wamberto Vasconcelos, Timothy J. Norman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)873-887
Number of pages15
JournalEngineering Applications of Artificial Intelligence
Issue number2
Early online date7 Jan 2013
Publication statusPublished - 1 Feb 2013


  • Knowledge-based resource selection
  • Logic Programming
  • Semantic web


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