2,022 research outputs found
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
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Applications of semantic web technology to support learning content development
The Semantic Web is based on ontology technology – a knowledge representation framework – at its core to make meaning explicit and more accessible to automatic processing. We discuss the potential of this technology for the development of content for learning technology systems. We survey seven application types demonstrating different forms of applications of ontologies and the Semantic Web in the development of learning technology systems. Ontology technologies can assist developers, instructors, and learners to organise, personalise, and publish learning content and to discover, generate, and compose learning content. A conceptual content development and deployment architecture allows us to distinguish and locate the different applications and to dis-cuss and assess the potential of the underlying technologies
Ontologies for the Interoperability of Heterogeneous Multi-Agent Systems in the scope of Energy and Power Systems
Tesis por compendio de publicaciones[ES]El sector eléctrico, tradicionalmente dirigido por monopolios y poderosas
empresas de servicios públicos, ha experimentado cambios significativos en las
últimas décadas. Los avances más notables son una mayor penetración de las
fuentes de energía renovable (RES por sus siglas en inglés) y la generación
distribuida, que han llevado a la adopción del paradigma de las redes inteligentes
(SG por sus siglas en inglés) y a la introducción de enfoques competitivos en los
mercados de electricidad (EMs por sus siglas en inglés) mayoristas y algunos
minoristas. Las SG emergieron rápidamente de un concepto ampliamente
aceptado en la realidad. La intermitencia de las fuentes de energía renovable y su
integración a gran escala plantea nuevas limitaciones y desafíos que afectan en
gran medida las operaciones de los EMs. El desafiante entorno de los sistemas de
potencia y energía (PES por sus siglas en inglés) refuerza la necesidad de
estudiar, experimentar y validar operaciones e interacciones competitivas,
dinámicas y complejas. En este contexto, la simulación, el apoyo a la toma de
decisiones, y las herramientas de gestión inteligente, se vuelven imprescindibles
para estudiar los diferentes mecanismos del mercado y las relaciones entre los
actores involucrados. Para ello, la nueva generación de herramientas debe ser
capaz de hacer frente a la rápida evolución de los PES, proporcionando a los
participantes los medios adecuados para adaptarse, abordando nuevos modelos
y limitaciones, y su compleja relación con los desarrollos tecnológicos y de
negocios.
Las plataformas basadas en múltiples agentes son particularmente
adecuadas para analizar interacciones complejas en sistemas dinámicos, como
PES, debido a su naturaleza distribuida e independiente. La descomposición de
tareas complejas en asignaciones simples y la fácil inclusión de nuevos datos y
modelos de negocio, restricciones, tipos de actores y operadores, y sus
interacciones, son algunas de las principales ventajas de los enfoques basados en
agentes. En este dominio, han surgido varias herramientas de modelado para
simular, estudiar y resolver problemas de subdominios específicos de PES. Sin
embargo, existe una limitación generalizada referida a la importante falta de
interoperabilidad entre sistemas heterogéneos, que impide abordar el problema
de manera global, considerando todas las interrelaciones relevantes existentes.
Esto es esencial para que los jugadores puedan aprovechar al máximo las
oportunidades en evolución. Por lo tanto, para lograr un marco tan completo aprovechando las herramientas existentes que permiten el estudio de partes
específicas del problema global, se requiere la interoperabilidad entre estos
sistemas.
Las ontologías facilitan la interoperabilidad entre sistemas heterogéneos al
dar un significado semántico a la información intercambiada entre las distintas
partes. La ventaja radica en el hecho de que todos los involucrados en un dominio
particular los conocen, comprenden y están de acuerdo con la conceptualización
allí definida. Existen, en la literatura, varias propuestas para el uso de ontologías
dentro de PES, fomentando su reutilización y extensión. Sin embargo, la mayoría
de las ontologías se centran en un escenario de aplicación específico o en una
abstracción de alto nivel de un subdominio de los PES. Además, existe una
considerable heterogeneidad entre estos modelos, lo que complica su integración
y adopción. Es fundamental desarrollar ontologías que representen distintas
fuentes de conocimiento para facilitar las interacciones entre entidades de
diferente naturaleza, promoviendo la interoperabilidad entre sistemas
heterogéneos basados en agentes que permitan resolver problemas específicos de
PES.
Estas brechas motivan el desarrollo del trabajo de investigación de este
doctorado, que surge para brindar una solución a la interoperabilidad de
sistemas heterogéneos dentro de los PES. Las diversas aportaciones de este
trabajo dan como resultado una sociedad de sistemas multi-agente (MAS por sus
siglas en inglés) para la simulación, estudio, soporte de decisiones, operación y
gestión inteligente de PES. Esta sociedad de MAS aborda los PES desde el EM
mayorista hasta el SG y la eficiencia energética del consumidor, aprovechando
las herramientas de simulación y apoyo a la toma de decisiones existentes,
complementadas con las desarrolladas recientemente, asegurando la
interoperabilidad entre ellas. Utiliza ontologías para la representación del
conocimiento en un vocabulario común, lo que facilita la interoperabilidad entre
los distintos sistemas. Además, el uso de ontologías y tecnologías de web
semántica permite el desarrollo de herramientas agnósticas de modelos para una
adaptación flexible a nuevas reglas y restricciones, promoviendo el razonamiento
semántico para sistemas sensibles al contexto
Ontologies Applied in Clinical Decision Support System Rules:Systematic Review
BackgroundClinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused.
ObjectiveOntologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules.
MethodsThe literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively.
ResultsCDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules.
ConclusionsOntologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules
Framework for collaborative knowledge management in organizations
Nowadays organizations have been pushed to speed up the rate of industrial transformation to high value products and services. The capability to agilely respond to new market demands became a strategic pillar for innovation, and knowledge management could support organizations to achieve that goal. However, current knowledge management approaches tend to be over complex or too academic, with interfaces difficult to manage, even more if cooperative handling is required. Nevertheless, in an ideal framework, both tacit and explicit knowledge management should be addressed to achieve knowledge handling with precise and semantically meaningful definitions. Moreover, with the increase of Internet usage, the amount of available information explodes. It leads to the observed progress in the creation of mechanisms to retrieve useful knowledge from the huge existent amount of information sources. However, a same knowledge representation of a thing could mean differently to different people and applications.
Contributing towards this direction, this thesis proposes a framework capable of gathering the knowledge held by domain experts and domain sources through a knowledge management system and transform it into explicit ontologies. This enables to build tools with advanced reasoning capacities with the aim to support enterprises decision-making processes. The author also intends to address the problem of knowledge transference within an among organizations. This will be done through a module (part of the proposed framework) for domain’s lexicon establishment which purpose is to represent and unify the understanding of the domain’s used semantic
Developing a semantic web-based distributed model management system: Experiences and lessons learned
Distributed model management systems (DMMSs) are decision support systems with a focus on managing decision models throughout the modeling lifecycle and across the extended enterprise. The advent and proliferation of web services and semantic web technologies offers the possibilities of sharing and reusing models in a distributed setting. This paper presents the design and implementation of a semantic web-based DMMS. Key lessons learned, technical and organizational issues encountered are summarized and directions for future research have been outlined. From a technical perspective, future research will need to explore the viability of tools specifically designed to facilitate the semantic annotation of models, specify and validate SA-SMML, and extend the white-box approach presented in this paper to other model types not amenable to structured modeling. From an organizational perspective, further research is needed in the areas of adoption issues and business models that would ensure the sustainable support for of such systems in the service enterprise
A MDD Strategy for developing Context-Aware Pervasive Systems
This master thesis proposes a methodological approach to develop context-aware pervasive systems based on ontologies and the Model-Driven Development (MDD) guidelines.Serral Asensio, E. (2008). A MDD Strategy for developing Context-Aware Pervasive Systems. http://hdl.handle.net/10251/12446Archivo delegad
Ontology mapping: a logic-based approach with applications in selected domains
In advent of the Semantic Web and recent standardization efforts, Ontology has quickly become a popular and core semantic technology. Ontology is seen as a solution provider to knowledge based systems. It facilitates tasks such as knowledge sharing, reuse and intelligent processing by computer agents. A key problem addressed by Ontology is the semantic interoperability problem. Interoperability in general is a common problem in different domain applications and semantic interoperability is the hardest and an ongoing research problem. It is required for systems to exchange knowledge and having the meaning of the knowledge accurately and automatically interpreted by the receiving systems. The innovation is to allow knowledge to be consumed and used accurately in a way that is not foreseen by the original creator.
While Ontology promotes semantic interoperability across systems by unifying their knowledge bases through consensual understanding, common engineering and processing practices, it does not solve the semantic interoperability problem at the global level. As individuals are increasingly empowered with tools, ontologies will eventually be created more easily and rapidly at a near individual scale. Global semantic interoperability between heterogeneous ontologies created by small groups of individuals will then be required.
Ontology mapping is a mechanism for providing semantic bridges between ontologies. While ontology mapping promotes semantic interoperability across ontologies, it is seen as the solution provider to the global semantic interoperability problem. However, there is no single ontology mapping solution that caters for all problem scenarios. Different applications would require different mapping techniques.
In this thesis, we analyze the relations between ontology, semantic interoperability and ontology mapping, and promote an ontology-based semantic interoperability solution. We propose a novel ontology mapping approach namely, OntoMogic. It is based on first order logic and model theory. OntoMogic supports approximate mapping and produces structures (approximate entity correspondence) that represent alignment results between concepts. OntoMogic has been implemented as a coherent system and is applied in different application scenarios. We present case studies in the network configuration, security intrusion detection and IT governance & compliance management domain. The full process of ontology engineering to mapping has been demonstrated to promote ontology-based semantic interoperability
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