Supporting threat analysis through description logic reasoning

2012 ◽  
Author(s):  
Jean Roy ◽  
Alexandre Bergeron Guyard
Keyword(s):  
Description Logic ◽  
Threat Analysis ◽  
Logic Reasoning

scholarly journals Pay-As-You-Go Description Logic Reasoning by Coupling Tableau and Saturation Procedures

2015 ◽  
Vol 54 ◽  
pp. 535-592 ◽  
Author(s):  
Andreas Steigmiller ◽  
Birte Glimm
Keyword(s):  
Description Logic ◽  
Description Logics ◽  
The Other ◽  
Web Ontology Language ◽  
Ontology Language ◽  
Specific Description ◽  
Wide Range ◽  
Detailed Evaluation ◽  
Logic Reasoning ◽  
The Web

Nowadays, saturation-based reasoners for the OWL EL profile of the Web Ontology Language are able to handle large ontologies such as SNOMED very efficiently. However, it is currently unclear how saturation-based reasoning procedures can be extended to very expressive Description Logics such as SROIQ--the logical underpinning of the current and second iteration of the Web Ontology Language. Tableau-based procedures, on the other hand, are not limited to specific Description Logic languages or OWL profiles, but even highly optimised tableau-based reasoners might not be efficient enough to handle large ontologies such as SNOMED. In this paper, we present an approach for tightly coupling tableau- and saturation-based procedures that we implement in the OWL DL reasoner Konclude. Our detailed evaluation shows that this combination significantly improves the reasoning performance for a wide range of ontologies.

Prolog Based Description Logic Reasoning

2008 ◽  
pp. 455-469 ◽  
Author(s):  
Gergely Lukácsy ◽  
Péter Szeredi ◽  
Balázs Kádár
Keyword(s):  
Description Logic ◽  
Logic Reasoning

Description Logic Reasoning with Syntactic Updates

2006 ◽  
Author(s):  
Christian Halashek-Wiener ◽  
Bijan Parsia ◽  
Evren Sirin
Keyword(s):  
Description Logic ◽  
Logic Reasoning

Efficient description logic reasoning in Prolog: The DLog system

2009 ◽  
Vol 9 (3) ◽  
pp. 343-414 ◽  
Author(s):  
GERGELY LUKÁCSY ◽  
PÉTER SZEREDI
Keyword(s):  
Information Integration ◽  
Description Logic ◽  
Large Data ◽  
Transformation Process ◽  
Large Data Sets ◽  
Data Sets ◽  
Prolog Program ◽  
Restricted Form ◽  
Logic Reasoning ◽  
Optimisation Techniques

AbstractTraditional algorithms for description logic (DL) instance retrieval are inefficient for large amounts of underlying data. As DL is becoming more and more popular in areas such as the Semantic Web and information integration, it is very important to have systems which can reason efficiently over large data sets. In this paper we present an approach to transform DL axioms, formalised in the $\mathcal{SHIQ}$ DL language, into a Prolog program under the unique name assumption. This transformation is performed with no knowledge about particular individuals: they are accessed dynamically during the normal Prolog execution of the generated program. This technique, together with the top-down Prolog execution, implies that only those pieces of data are accessed that are indeed important for answering the query. This makes it possible to store the individuals in a database instead of memory, which results in better scalability and helps in using DL ontologies directly on top of existing information sources. The transformation process consists of two steps: (1) the DL axioms are converted to first-order clauses of a restricted form, and (2) a Prolog program is generated from these clauses. Step (2), which is the focus of the present paper, actually works on more general clauses than those obtainable by applying step (1) to a $\mathcal{SHIQ}$ knowledge base. We first present a base transformation, the output of which can be either executed using a simple interpreter or further extended to executable Prolog code. We then discuss several optimisation techniques, applicable to the output of the base transformation. Some of these techniques are specific to our approach, while others are general enough to be interesting for DL reasoner implementors not using Prolog. We give an overview of DLog, a DL reasoner in Prolog, which is an implementation of the techniques outlined above. We evaluate the performance of DLog and compare it to some widely used DL reasoners, such as RacerPro, Pellet and KAON2.

scholarly journals A Breakdown of System of Systems Needs Using Architecture Frameworks, Ontologies and Description Logic Reasoning

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 118
Author(s):  
Ludvig Knöös Franzén ◽  
Ingo Staack ◽  
Petter Krus ◽  
Christopher Jouannet ◽  
Kristian Amadori
Keyword(s):  
Description Logic ◽  
Design Space ◽  
Singular System ◽  
System Of Systems ◽  
Space Representation ◽  
System Perspective ◽  
Initial Design ◽  
Architecture Framework ◽  
Architecture Frameworks ◽  
Logic Reasoning

Aerospace systems are connected with the operational environment and other systems in general. The focus in aerospace product development is consequently shifting from a singular system perspective to a System-of-Systems (SoS) perspective. This increasing complexity gives rise to new levels of uncertainty that must be understood and managed to produce aerospace solutions for an ever-changing future. This paper presents an approach to using architecture frameworks, and ontologies with description logic reasoning capabilities, to break down SoS needs into required capabilities and functions. The intention of this approach is to provide a consistent way of obtaining the functions to be realized in order to meet the overarching capabilities and needs of an SoS. The breakdown with an architecture framework results in an initial design space representation of functions to be performed. The captured knowledge is then represented in an ontology with description logic reasoning capabilities, which provides a more flexible way to expand and process the initial design space representation obtained from the architecture framework. The proposed approach is ultimately tested in a search and rescue case study, partly based on the operations of the Swedish Maritime Administration. The results show that it is possible to break down SoS needs in a consistent way and that ontology with description logic reasoning can be used to process the captured knowledge to both expand and reduce an available design space representation.

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