25,549 research outputs found
Unifying multilevel modelling through ontologies
In the last decades, the multilevel problem has received increasing attention in the conceptual modelling and semantic web communities. Recently, we proposed a solution to this problem in the context of ontological modelling which consists in extending the Web Ontology Language OWL with a new multilevel constructor that equates instances to classes. In this work we highlight the advantages of exploiting the reasoning capabilities of OWL ontologies with the proposed multilevel constructor by analizing requirements from a real-world
application on the accounting domain
Open world reasoning in semantics-aware access control: A preliminary study
We address the relationships between theoretical foundations of Description Logics and practical applications of security-oriented Semantic Web techniques. We first describe the advantages of semantics-aware Access Control and review the state of the art; we also introduce the basics of Description Logics and the novel semantics they share. Then we translate the principle underlying the Little House Problem of DL into a real-world use case: by applying Open World Reasoning to the Knowledge Base modelling a Virtual Organization, we derive information not achievable with traditional Access Control methodologies. With this example, we also show that a general problem such as ontology mapping can take advantage of the enhanced semantics underlying OWL Lite and OWL DL to handle under-specified concepts
Reasoning Algebraically with Description Logics
Semantic Web applications based on the Web Ontology Language (OWL) often
require the use of numbers in class descriptions for expressing
cardinality restrictions on properties or even classes. Some of these
cardinalities are specified explicitly, but quite a few are entailed and
need to be discovered by reasoning procedures. Due to the Description
Logic (DL) foundation of OWL, those reasoning services are offered by DL
reasoners. Existing DL reasoners employ reasoning procedures that are
arithmetically uninformed and substitute arithmetic reasoning by "don't
know" non-determinism in order to cover all possible cases. This lack of
information about arithmetic problems dramatically degrades the
performance of DL reasoners in many cases, especially with ontologies
relying on the use of Nominals and Qualied Cardinality Restrictions.
The contribution of this thesis is twofold: on the theoretical level, it
presents algebra�ic reasoning with DL (ReAl DL) using a sound, complete,
and terminating reasoning procedure for the DL SHOQ. ReAl DL combines
tableau reasoning procedures with algebraic methods, namely Integer
Programming, to ensure arithmetically better informed reasoning. SHOQ
extends the standard DL ALC with transitive roles, role hierarchies,
qualified cardinality restrictions (QCRs), and nominals, and forms an
expressive subset of OWL. Although the proposed algebraic tableau is
double exponential in the worst case, it deals with cardinalities with
an additional level of information and properties that make the calculus
amenable and well suited for optimizations. In order for ReAl DL to have
a practical merit, suited optimizations are proposed towards achieving
an efficient reasoning approach that addresses the sources of complexity
related to nominals and QCRs. On the practical level, a running
prototype reasoner (HARD) is implemented based on the proposed calculus
and optimizations. HARD is used to evaluate the practical merit of ReAl
DL, as well as the effectiveness of the proposed optimizations.
Experimental results based on real world and synthetic ontologies show
that ReAl DL outperforms existing reasoning approaches in handling the
interactions between nominals and QCRs. ReAl DL also comes with some
interesting features such as the ability to handle ontologies with
cyclic descriptions without adopting special blocking strategies. ReAl
DL can form a basis to provide more efficient reasoning support for
ontologies using nominals or QCRs
An MDE-based Methodology for Closed-World Integrity Constraint Checking in the Semantic Web
Ontology-based data-centric systems support open-world reasoning. Therefore, for these systems, Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) are not suitable for expressing integrity constraints based on the closed-world assumption. Thus, the requirement of integrating the open-world assumption of OWL/SWRL with closed-world integrity constraint checking is inevitable. SPARQL, recommended by World Wide Web (W3C), is a query language for RDF graphs, and many research studies have shown that it is a perfect candidate for closed-world constraint checking for ontology-based data-centric applications. In this regard, many research studies have been performed to transform integrity constraints into SPARQL queries where some studies have shown the limitations of partial expressivity of knowledge bases while performing the indirect transformations, whereas others are limited to a platform-specific implementation. To address these issues, this paper presents a flexible and formal methodology that employs Model-Driven Engineering (MDE) to model closed-world integrity constraints for open-world reasoning. The proposed approach offers semantic validation of data by expressing integrity constraints at both the model level and the code level. Moreover, straightforward transformations from OWL/SWRL to SPARQL can be performed. Finally, the methodology is demonstrated via a real-world case study of water observations data
Introducing Defeasibility into OWL Ontologies
In recent years, various approaches have been developed for repre- senting and reasoning with exceptions in OWL. The price one pays for such ca- pabilities, in terms of practical performance, is an important factor that is yet to be quantified comprehensively. A major barrier is the lack of naturally oc- curring ontologies with defeasible features - the ideal candidates for evaluation. Such data is unavailable due to absence of tool support for representing defea- sible features. In the past, defeasible reasoning implementations have favoured automated generation of defeasible ontologies. While this suffices as a prelimi- nary approach, we posit that a method somewhere in between these two would yield more meaningful results. In this work, we describe a systematic approach to modify real-world OWL ontologies to include defeasible features, and we ap- ply this to the Manchester OWL Repository to generate defeasible ontologies for evaluating our reasoner DIP (Defeasible-Inference Platform). The results of this evaluation are provided together with some insights into where the performance bottle-necks lie for this kind of reasoning. We found that reasoning was feasible on the whole, with surprisingly few bottle-necks in our evaluation
Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web
Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions
OWL-POLAR : semantic policies for agent reasoning
The original publication is available at www.springerlink.comPostprin
A multi-INT semantic reasoning framework for intelligence analysis support
Lockheed Martin Corp. has funded research to generate a framework
and methodology for developing semantic reasoning applications to support the
discipline oflntelligence Analysis. This chapter outlines that framework, discusses
how it may be used to advance the information sharing and integrated analytic
needs of the Intelligence Community, and suggests a system I software
architecture for such applications
OWL-POLAR : A Framework for Semantic Policy Representation and Reasoning
Peer reviewedPreprin
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