125 research outputs found
Optimizing the computation of overriding
We introduce optimization techniques for reasoning in DLN---a recently
introduced family of nonmonotonic description logics whose characterizing
features appear well-suited to model the applicative examples naturally arising
in biomedical domains and semantic web access control policies. Such
optimizations are validated experimentally on large KBs with more than 30K
axioms. Speedups exceed 1 order of magnitude. For the first time, response
times compatible with real-time reasoning are obtained with nonmonotonic KBs of
this size
A survey of large-scale reasoning on the Web of data
As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning
Scalable RDF compression with MapReduce and HDT
El uso de RDF para publicar datos semánticos se ha incrementado de forma notable en los últimos años. Hoy los datasets son tan grandes y están tan interconectados que su procesamiento presenta problemas de escalabilidad. HDT es una representación compacta de RDF que pretende minimizar el consumo de espacio a la vez que proporciona capacidades de consulta. No obstante, la generación de HDT a partir de formatos en texto de RDF es una tarea costosa en tiempo y recursos. Este trabajo estudia el uso de MapReduce, un framework para el procesamiento distribuido de grandes cantidades de datos, para la tarea de creación de estructuras HDT a partir de RDF, y analiza las mejoras obtenidas tanto en recursos como en tiempo frente a la creación de dichas estructuras en un proceso mono-nodo.Departamento de Informática (Arquitectura y TecnologÃa de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Máster en Investigación en TecnologÃas de la Información y las Comunicacione
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
OPTIMIZATION OF NONSTANDARD REASONING SERVICES
The increasing adoption of semantic technologies and the corresponding increasing complexity of application requirements are motivating extensions to the standard reasoning paradigms and services supported by such technologies. This thesis focuses on two of such extensions: nonmonotonic reasoning and inference-proof access control.
Expressing knowledge via general rules that admit exceptions is an approach that has been commonly adopted for centuries in areas such as law and science, and more recently in object-oriented programming and computer security. The experiences in developing complex biomedical knowledge bases reported in the literature show that a direct support to defeasible properties and exceptions would be of great help.
On the other hand, there is ample evidence of the need for knowledge confidentiality measures. Ontology languages and Linked Open Data are increasingly being used to encode the private knowledge of companies and public organizations. Semantic Web techniques facilitate merging different sources of knowledge and extract implicit information, thereby putting at risk security and the privacy of individuals. But the same reasoning capabilities can be exploited to protect the confidentiality of knowledge.
Both nonmonotonic inference and secure knowledge base access rely on nonstandard reasoning procedures. The design and realization of these algorithms in a scalable way (appropriate to the ever-increasing size of ontologies and knowledge bases) is carried out by means of a diversified range of optimization techniques such as appropriate module extraction and incremental reasoning. Extensive experimental evaluation shows the efficiency of the developed optimization techniques: (i) for the first time performance compatible with real-time reasoning is obtained for large nonmonotonic ontologies, while (ii) the secure ontology access control proves to be already compatible with practical use in the e-health application scenario.
Computer recommendations for an automatic approach and landing system for V/STOL aircraft. Volume 2 - Equations
Automatic approach and landing system for V/STOL aircraf
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