An infrastructure for context-awareness based on first order logic

Ranganathan, Anand; Campbell, Roy H.

doi:10.1007/s00779-003-0251-x

Cited by 263 publications

(131 citation statements)

References 19 publications

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“…Various architectures for context-awareness are based on ontological formalisms augmented with generic FOL rules, either in a loosely [21,36] or tightly coupled [39,40] fashion. FOL rules are the most expressive rules that were proposed in the context-awareness literature to extend ontological languages; hence, obviously OWL 2, whose underlying description logic is a fragment of FOL, is less expressive than those languages.…”

Section: Discussionmentioning

confidence: 99%

OWL 2 modeling and reasoning with complex human activities

Riboni

Bettini

2011

Pervasive and Mobile Computing

135

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In recent years, there has been a growing interest in the adoption of ontologies and ontological reasoning to automatically recognize complex context data such as human activities. In particular, the Web Ontology Language (OWL) emerged as the language of choice, being a standard for the Semantic Web, and supported by a number of tools for knowledge engineering and reasoning. However, the limitations of OWL 1 in terms of expressiveness have been recognized in various fields, and important research e↵orts have been made to extend the language while preserving decidability of its OWL 1 DL fragment. The result of such work is OWL 2. In this paper we investigate the use of OWL 2 for modeling complex activities and reasoning with them. We show that the new language constructors of OWL 2 overcome the main limitations of OWL 1 for the representation of activities; OWL 2 axioms can be used to represent certain rules and rule-based reasoning previously demanded to hybrid approaches, with the advantage of having a unique semantics, avoiding potential inconsistencies. Then, we propose a system architecture showing the integration of a novel OWL 2 activity ontology and reasoning modules with distributed modules for sensor data aggregation and reasoning. The feasibility of our solution is shown by an extensive experimental evaluation with simulations of di↵erent intelligent environments.

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Section: Discussionmentioning

confidence: 99%

OWL 2 modeling and reasoning with complex human activities

Riboni

Bettini

2011

Pervasive and Mobile Computing

135

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“…Some research work exists which addresses the management of subsets of the interference problem , e.g. [KMW03], [MD06], [SHW05], or [RC03]. Other work focuses on the realization of frameworks to detect and resolve problems between multiple applications such as [MD06] and [BRK06].…”

Section: Research Aimmentioning

confidence: 99%

Application Coordination in Pervasive Systems

Majuntke¹

2013

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“…Ranganathan [19] proposes to use a first-order logic to model a user's context and reason about it. To reason with context information stored in multiple hosts, each context provider on those hosts provides an interface that handles a query from a remote host.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, a system in pervasive computing needs to consider the user's context and change its behavior dynamically based on a set of rules. Many systems [7,10,19,22] in pervasive computing apply a logic-based language to express those rules, since it also makes it possible to define a context model where a contextual fact is expressed with a boolean predicate. Besides defining triggering actions [22] of pervasive applications (e.g., a smart meeting room), a logical language provides a way to infer high-level context information [13,19], such as a user's activity, from raw sensor data.…”

Section: Introductionmentioning

confidence: 99%

“…Many systems [7,10,19,22] in pervasive computing apply a logic-based language to express those rules, since it also makes it possible to define a context model where a contextual fact is expressed with a boolean predicate. Besides defining triggering actions [22] of pervasive applications (e.g., a smart meeting room), a logical language provides a way to infer high-level context information [13,19], such as a user's activity, from raw sensor data. One promising application of the logic-based approach is a context-sensitive authorization system [1,2,6,8,11,18,24] that considers a requester's context as well as his identity to make a granting decision; the system derives the granting decision (true or false) with a set of rules encoding policies and facts encoding context information.…”

Section: Introductionmentioning

confidence: 99%

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Scalability in a Secure Distributed Proof System

Minami

Kotz

2006

Lecture Notes in Computer Science

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Abstract.A logic-based language is often adopted in systems for pervasive computing, because it provides a convenient way to define rules that change the behavior of the systems dynamically. Those systems might define rules that refer to the users' context information to provide context-aware services. For example, a smart-home application could define rules referring to the location of a user to control the light of a house automatically. In general, the context information is maintained in different administrative domains, and it is, therefore, desirable to construct a proof in a distributed way while preserving each domain's confidentiality policies. In this paper, we introduce such a system, a secure distributed proof system for context-sensitive authorization and show that our novel caching and revocation mechanism improves the performance of the system, which depends on public key cryptographic operations to protect confidential information in rules and facts. Our revocation mechanism maintains dependencies among facts and recursively revokes across multiple hosts all the cached facts that depend on a fact that has become invalid. Our initial experimental results show that our caching mechanism, which maintains both positive and negative facts, significantly reduces the latency for handling a logical query.

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An infrastructure for context-awareness based on first order logic

Cited by 263 publications

References 19 publications

OWL 2 modeling and reasoning with complex human activities

OWL 2 modeling and reasoning with complex human activities

Application Coordination in Pervasive Systems

Scalability in a Secure Distributed Proof System

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