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Our study in this paper shows that ontology based context model is feasible and necessary for supporting context modeling and reasoning in pervasive computing environments

Ontology Based Context Modeling and Reasoning using OWL

PerCom Workshops, pp.18-22, (2004)

被引用1597|浏览178
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摘要

Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchic...更多

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简介
  • Recent years have witnessed rapid advances in the enabling technologies for pervasive computing.
  • With the advance of context aware computing, there is a increasing need for developing formal context models to facilitate context representation, context sharing and semantic interoperability of heterogeneous systems.
  • In previous works, both informal and formal context models have been proposed.
  • None of them has addressed formal knowledge sharing, or has shown a quantitative evaluation for the feasibility of context reasoning in pervasive computing environments, where the authors always have to face resource-constraint devices
重点内容
  • Recent years have witnessed rapid advances in the enabling technologies for pervasive computing
  • We present an ontology-based formal context model to address critical issues including formal context representation, knowledge sharing and logic based context reasoning
  • The ontology reasoner we have tested is associated with the description logic rule set consisting of all 111 axioms entailed by OWL-Lite, and the situation reasoner applies a rule set containing of 10 forward-chaining rules that we have partially described in table 2
  • Our study in this paper shows that ontology based context model is feasible and necessary for supporting context modeling and reasoning in pervasive computing environments
  • We have conducted a performance study to evaluate the feasibility for context reasoning in pervasive computing environments
  • The work of this paper is a part of our ongoing context aware service infrastructure [9], which aims to provide an open, reusable infrastructure for essential context aware mechanisms
结论
  • The authors' study in this paper shows that ontology based context model is feasible and necessary for supporting context modeling and reasoning in pervasive computing environments.
  • The authors have implemented the CONON and logic based context reasoning schemes.
  • The authors have conducted a performance study to evaluate the feasibility for context reasoning in pervasive computing environments.
  • The authors' design explores Web Ontology Language for context modeling and knowledge sharing, hybrid reasoning and learning for context interpretation, and Semantic Web query for expressive context query and resource discovery
总结
  • Recent years have witnessed rapid advances in the enabling technologies for pervasive computing.
  • None of them has addressed formal knowledge sharing, or has shown a quantitative evaluation for the feasibility of context reasoning in pervasive computing environments, where we always have to face resource-constraint devices.
  • We present an ontology-based formal context model to address critical issues including formal context representation, knowledge sharing and logic based context reasoning.
  • We will present the detailed design of our context model and logic based context reasoning scheme.
  • There are several reasons for developing context models based on ontology: x Knowledge Sharing.
  • Context-aware computing can exploit various existing logic reasoning mechanisms to deduce high-level, conceptual context from low-level, raw context, and to check and solve inconsistent context knowledge due to imperfect sensing.
  • We believe that Web ontology and other Semantic Web technologies can be employed in modeling and reasoning about context information in pervasive computing environments.
  • We present an extensible CONtext ONtology (CONON) for modeling context in pervasive computing environments.
  • The objectives of our context model include modeling a set of upper-level entities, and providing flexible extensibility to add specific concepts in different application domains.
  • Besides general classes defined in CONON upper ontology, a number of concrete sub-classes are defined to model specific context in a given environment.
  • Through the creation of userdefined reasoning rules within the entailment of firstorder logic, a wide range of higher-level, conceptual context such as l what the user is doingl can be deduced from relevant low-level context.
  • Table 3 shows the user-defined context reasoning rules that are employed to derive user-s situation in the smart phone scenario.
  • The objectives of these experiments are to conduct a quantitative feasibility study for logic reasoning in pervasive computing environments, and provide useful information for the implementation of context reasoning.
  • Context reasoners are built using Jena2 Semantic Web Toolkit [7], which supports rule-based inference over OWL/RDF graphs.
  • From the quantitative study of runtime performance, we have a number of observations that are useful for the design of context model and context reasoning mechanism: First, context reasoning is generally feasible for non-time-critical applications.
  • Context aware services in different domains shares most general concepts, while there exists significant difference between the ontologies they need.
  • Our study in this paper shows that ontology based context model is feasible and necessary for supporting context modeling and reasoning in pervasive computing environments.
  • We have conducted a performance study to evaluate the feasibility for context reasoning in pervasive computing environments.
  • Our design explores Web Ontology Language for context modeling and knowledge sharing, hybrid reasoning and learning for context interpretation, and Semantic Web query for expressive context query and resource discovery
表格
  • Table1: Parts of OWL ontology reasoning rules
  • Table2: Reasoning about location using ontology
  • Table3: User-defined context reasoning rules
Download tables as Excel
基金
  • Proposes an OWL encoded context ontology for modeling context in pervasive computing environments, and for supporting logicbased context reasoning
  • Has studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context
  • Evaluates the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, walwayss have to deal carefully with the limitation of computational resources
  • Presents an ontology-based formal context model to address critical issues including formal context representation, knowledge sharing and logic based context reasoning
引用论文
  • Journal, Vol. 16(2-4), pp. 97-166, 2001.
    Google ScholarFindings
  • Tim Kindberg, et al. People, Places, Things: Web 2000-16, HP Labs, 2000.
    Google ScholarFindings
  • Karen Henricksen, et al., Modeling Context Information in Pervasive Computing Systemsi, Pervasive 2002.
    Google ScholarFindings
  • Anand Ranganathan, et al. A Middleware for Context-Aware Agents in Ubiquitous Computing Environmentse, USENIX International Middleware Conference, 2002.
    Google ScholarLocate open access versionFindings
  • T. Berners-Lee, J. Hendler, and O. Lassila,, The Semantic WebT, Scientific American may 2001.
    Google ScholarFindings
  • F. van Harmelen, et al. eOwl Web Ontology Language ReferenceO, http://www.w3.org/TR/owl-ref/, 2002.
    Findings
  • Toolkit: http://www.hpl.hp.com/semweb/jena2.htm.
    Findings
  • Ontology: http://www.cyc.com/cycdoc/vocab/vocab-toc.html.
    Findings
  • Daqing Zhang, Xiaohang Wang, Karianto Leman, and Conference on Smart Homes and Health Telematics, 2003, France.
    Google ScholarLocate open access versionFindings
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