Supporting Rule System Interoperability on the Semantic Web with SWRL

O’Connor, Martin J.; Knublauch, Holger; Tu, Samson W.; Grosof, Benjamin N.; Dean, Mike; Grosso, William; Musen, Mark A.

doi:10.1007/11574620_69

Cited by 175 publications

(79 citation statements)

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“…Mapping between OWL ontologies is typically done using SWRL [13], the Semantic Web Rule Language, which combines OWL and RuleML 3 concepts. SWRL rules are an implication between an antecedent and a consequent, both comprised of multiple atoms.…”

Section: Swrl Rulesmentioning

confidence: 99%

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Application of Ontology Translation

Ressler

Dean²,

Benson³

et al. 2007

The Semantic Web

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Abstract. An ontology provides a precise specification of the vocabulary used by a community of interest (COI). Multiple communities of interest may describe the same concept using the same or different terms. When such communities interact, ontology alignment and translation is required. This is typically a time consuming process. This paper describes Snoggle, an open source tool designed to ease development of ontology translation rules, and discusses its application to geospatial ontologies.

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Section: Swrl Rulesmentioning

confidence: 99%

“…Snoggle differs from other SWRL rule editors such as Protégé [13], the Object Rule Editor (ORE) [10], and RuleVISor [9] in supporting a graph-based visual paradigm and in focusing on ontology translation through the use of multiple ontology browsers, a divided Canvas, and coverage indicators.…”

Section: Related Workmentioning

confidence: 99%

Application of Ontology Translation

Ressler

Dean²,

Benson³

et al. 2007

The Semantic Web

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“…In this dynamic context, the Validator agent automatically assists in the correctness of the model as a whole by periodically checking a set of rules defined for the profile. We propose using SWRL [7] rules as a way of supporting the semantic consistence and ontological reasoning. These rules are ontological expressions with an antecedent and a consequent that allows the Validator agent to detect and infer problematic situations.…”

Section: Semantic Conflict Detectionmentioning

confidence: 99%

Multiagent System for Detecting and Solving Design-Time Conflicts in Civil Infrastructure

Faus

Grimaldo

Barber

2012

Advances in Intelligent and Soft Computing

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One typical source of problems in the Civil Infrastructure domain is the distributed and collaborative nature of the projects in which different profiles of engineers contribute with designs devoted to the interest of their field of expertise. Thus, situations in which there are different conflicts of interests are quite common. A conflict refers to a situation in which the actions of an engineer collide with the interests of other engineers. In this paper, we present a multi-agent system that, thanks to the use of ontologies and rules on those ontologies, is able to detect profilespecific conflict situations and solve them according to the preferences of the parties involved in the conflict. The conflict solving is based on the Multi Agent Resource Allocation (MARA) theory. The system is applied to a real use case of an urban development where both the road network and the buildings are designed.

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“…Analysis of security policy configuration is performed using the Semantic Web Rule Language SWRL [22]. SWRL complements DL providing the ability to infer additional information from DL constrained ontologies.…”

Section: Threshold Satisfiability Analysismentioning

confidence: 99%

“…Protégé interfaces with a DL based reasoner called Pellet providing model classification and consistency [25]. In conjunction to DL reasoning support, the SWRL Protégé plug-in (SWRLTab), allows for the creation of horn-like logic rules that interfaces with an expert system called Jess [26,22]. In practice, a domain expert using such tools can avoid or at least limit having to become an expert in DL and/or OWL notation, as these semantic editors and underlying reasoning tools hide much of the underlying complexity.…”

Section: Configuration Recommendation Synthesismentioning

confidence: 99%

An Approach to Security Policy Configuration Using Semantic Threat Graphs

Foley

Fitzgerald

2009

Data and Applications Security XXIII

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Abstract. Managing the configuration of heterogeneous enterprise security mechanisms is a wholly complex task. The effectiveness of a configuration may be constrained by poor understanding and/or management of the overall security policy requirements, which may, in turn, unnecessarily expose the enterprise to known threats. This paper proposes a threat management approach, whereby knowledge about the effectiveness of mitigating countermeasures is used to guide the autonomic configuration of security mechanisms. This knowledge is modeled in terms of Semantic Threat Graphs, a variation of the traditional Threat/Attack Tree, extended in order to relate semantic information about security configuration with threats, vulnerabilities and countermeasures. An ontology-based approach to representing and reasoning over this knowledge is taken. A case study on Network Access Controls demonstrates how threats can be analyzed and how automated configuration recommendations can be made based on catalogues of best-practice countermeasures.

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Supporting Rule System Interoperability on the Semantic Web with SWRL

Cited by 175 publications

References 1 publication

Application of Ontology Translation

Application of Ontology Translation

Multiagent System for Detecting and Solving Design-Time Conflicts in Civil Infrastructure

An Approach to Security Policy Configuration Using Semantic Threat Graphs

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