1,134 research outputs found
Information system support in construction industry with semantic web technologies and/or autonomous reasoning agents
Information technology support is hard to find for the early design phases of the architectural design process. Many of the existing issues in such design decision support tools appear to be caused by a mismatch between the ways in which designers think and the ways in which information systems aim to give support. We therefore started an investigation of existing theories of design thinking, compared to the way in which design decision support systems provide information to the designer. We identify two main strategies towards information system support in the early design phase: (1) applications for making design try-outs, and (2) applications as autonomous reasoning agents. We outline preview implementations for both approaches and indicate to what extent these strategies can be used to improve information system support for the architectural designer
Approximate Assertional Reasoning Over Expressive Ontologies
In this thesis, approximate reasoning methods for scalable assertional reasoning are provided whose computational properties can be established in a well-understood way, namely in terms of soundness and completeness, and whose quality can be analyzed in terms of statistical measurements, namely recall and precision. The basic idea of these approximate reasoning methods is to speed up reasoning by trading off the quality of reasoning results against increased speed
Reasoning about Explanations for Negative Query Answers in DL-Lite
In order to meet usability requirements, most logic-based applications
provide explanation facilities for reasoning services. This holds also for
Description Logics, where research has focused on the explanation of both TBox
reasoning and, more recently, query answering. Besides explaining the presence
of a tuple in a query answer, it is important to explain also why a given tuple
is missing. We address the latter problem for instance and conjunctive query
answering over DL-Lite ontologies by adopting abductive reasoning; that is, we
look for additions to the ABox that force a given tuple to be in the result. As
reasoning tasks we consider existence and recognition of an explanation, and
relevance and necessity of a given assertion for an explanation. We
characterize the computational complexity of these problems for arbitrary,
subset minimal, and cardinality minimal explanations
Implementing OBDA for an end-user query answering service on an educational ontology
In the age where productivity of society is no longer defined by the amount of information
generated, but from the quality and assertiveness that a set of data may potentially hold,
the right questions to do depends on the semantic awareness capability that an
information system could evolve into. To address this challenge, in the last decade,
exhaustive research has been done in the Ontology Based Data Access (OBDA)
paradigm.
A conspectus of the most promising technologies with data integration capabilities and
the foundations where they rely are documented in this memory as a point of reference
for choosing tools that supports the incorporation of a conceptual model under a OBDA
method. The present study provides a practical approach for implementing an ontology
based data access service, to educational context users of a Learning Analytics initiative,
by means of allowing them to formulate intuitive enquiries with a familiar domain
terminology on top of a Learning Management System. The ontology used was
completely transformed to semantic linked data standards and some data mappings for
testing were included. Semantic Linked Data technologies exposed in this document may
exert modernization to environments in which object oriented and relational paradigms
may propagate heterogeneous and contradictory requirements. Finally, to validate the
implementation, a set of queries were constructed emulating the most relevant dynamics
of the model regarding the dataset nature
Automatically selecting patients for clinical trials with justifications
Clinical trials are human research studies that are used to evaluate the effectiveness
of a surgical, medical, or behavioral intervention. They have been widely used by researchers
to determine whether a new treatment, such as a new medication, is safe and
effective in humans. A clinical trial is frequently performed to determine whether a new
treatment is more successful than the current treatment or has less harmful side effects.
However, clinical trials have a high failure rate. One method applied is to find patients
based on patient records. Unfortunately, this is a difficult process. This is because this
process is typically performed manually, making it time-consuming and error-prone.
Consequently, clinical trial deadlines are often missed, and studies do not move forward.
Time can be a determining factor for success. Therefore, it would be advantageous to have
automatic support in this process. Since it is also important to be able to validate whether
the patients were selected correctly for the trial, avoiding eventual health problems, it
would be important to have a mechanism to present justifications for the selected patients.
In this dissertation, we present one possible solution to solve the problem of patient
selection for clinical trials. We developed the necessary algorithms and created a simple
and intuitive web application that features the selection of patients for clinical trials automatically.
This was achieved by combining knowledge expressed in different formalisms.
We integrated medical knowledge using ontologies, with criteria that were expressed
using nonmonotonic rules. To address the validation procedure automatically, we developed
a mechanism that generates the justifications for each selection together with the
results of the patients who were selected.
In the end, it is expected that a user can easily enter a set of trial criteria, and the
application will generate the results of the selected patients and their respective justifications,
based on the criteria inserted, medical information and a database of patient
information.Os ensaios clínicos são estudos de pesquisa em humanos, utilizados para avaliar a
eficácia de uma intervenção cirúrgica, médica ou comportamental. Estes estudos, têm
sido amplamente utilizados pelos investigadores para determinar se um novo tratamento,
como é o caso de um novo medicamento, é seguro e eficaz em humanos. Um ensaio clínico
é realizado frequentemente, para determinar se um novo tratamento tem mais sucesso
do que o tratamento atual ou se tem menos efeitos colaterais prejudiciais.
No entanto, os ensaios clínicos têm uma taxa de insucesso alta. Um método aplicado
é encontrar pacientes com base em registos. Infelizmente, este é um processo difícil.
Isto deve-se ao facto deste processo ser normalmente realizado à mão, o que o torna
demorado e propenso a erros. Consequentemente, o prazo dos ensaios clínicos é muitas
vezes ultrapassado e os estudos acabam por não avançar. O tempo pode ser por vezes um
fator determinante para o sucesso. Seria então vantajoso ter algum apoio automático neste
processo. Visto que também seria importante validar se os pacientes foram selecionados
corretamente para o ensaio, evitando até eventuais problemas de saúde, seria importante
ter um mecanismo que apresente justificações para os pacientes selecionados.
Nesta dissertação, apresentamos uma possível solução para resolver o problema da
seleção de pacientes para ensaios clínicos, através da criação de uma aplicação web, intuitiva
e fácil de utilizar, que apresenta a seleção de pacientes para ensaios clínicos de
forma automática. Isto foi alcançado através da combinação de conhecimento expresso
em diferentes formalismos. Integrámos o conhecimento médico usando ontologias, com
os critérios que serão expressos usando regras não monotónicas. Para tratar do processo
de validação, desenvolvemos um mecanismo que gera justificações para cada seleção
juntamente com os resultados dos pacientes selecionados.
No final, é esperado que o utilizador consiga inserir facilmente um conjunto de critérios
de seleção, e a aplicação irá gerar os resultados dos pacientes selecionados e as
respetivas justificações, com base nos critérios inseridos, informações médicas e uma base
de dados com informações dos pacientes
A study of existing Ontologies in the IoT-domain
Several domains have adopted the increasing use of IoT-based devices to
collect sensor data for generating abstractions and perceptions of the real
world. This sensor data is multi-modal and heterogeneous in nature. This
heterogeneity induces interoperability issues while developing cross-domain
applications, thereby restricting the possibility of reusing sensor data to
develop new applications. As a solution to this, semantic approaches have been
proposed in the literature to tackle problems related to interoperability of
sensor data. Several ontologies have been proposed to handle different aspects
of IoT-based sensor data collection, ranging from discovering the IoT sensors
for data collection to applying reasoning on the collected sensor data for
drawing inferences. In this paper, we survey these existing semantic ontologies
to provide an overview of the recent developments in this field. We highlight
the fundamental ontological concepts (e.g., sensor-capabilities and
context-awareness) required for an IoT-based application, and survey the
existing ontologies which include these concepts. Based on our study, we also
identify the shortcomings of currently available ontologies, which serves as a
stepping stone to state the need for a common unified ontology for the IoT
domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of
Thing
Challenges in Bridging Social Semantics and Formal Semantics on the Web
This paper describes several results of Wimmics, a research lab which names
stands for: web-instrumented man-machine interactions, communities, and
semantics. The approaches introduced here rely on graph-oriented knowledge
representation, reasoning and operationalization to model and support actors,
actions and interactions in web-based epistemic communities. The re-search
results are applied to support and foster interactions in online communities
and manage their resources
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