310 research outputs found
An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices
The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception – explanation and discrimination – and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale
Knowledge base exchange: the case of OWL 2 QL
In this article, we define and study the problem of exchanging knowledge between a source and a target knowledge base (KB), connected through mappings. Differently from the traditional database exchange setting, which considers only the exchange of data, we are interested in exchanging implicit knowledge. As representation formalism we use Description Logics (DLs), thus assuming that the source and target KBs are given as a DL TBox+ABox, while the mappings have the form of DL TBox assertions. We define a general framework of KB exchange, and study the problem of translating the knowledge in the source KB according to the mappings expressed in OWL 2 QL, the profile of the standard Web Ontology Language OWL 2 based on the description logic DL-LiteR. We develop novel game- and automata-theoretic techniques, and we provide complexity results that range from NLogSpace to ExpTim
Infectious Disease Ontology
Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain
Non classical concept representation and reasoning in formal ontologies
Formal ontologies are nowadays widely considered a standard tool for knowledge
representation and reasoning in the Semantic Web. In this context, they are expected to
play an important role in helping automated processes to access information. Namely:
they are expected to provide a formal structure able to explicate the relationships
between different concepts/terms, thus allowing intelligent agents to interpret, correctly,
the semantics of the web resources improving the performances of the search
technologies.
Here we take into account a problem regarding Knowledge Representation in general,
and ontology based representations in particular; namely: the fact that knowledge
modeling seems to be constrained between conflicting requirements, such as
compositionality, on the one hand and the need to represent prototypical information on
the other. In particular, most common sense concepts seem not to be captured by the
stringent semantics expressed by such formalisms as, for example, Description Logics
(which are the formalisms on which the ontology languages have been built). The aim
of this work is to analyse this problem, suggesting a possible solution suitable for
formal ontologies and semantic web representations.
The questions guiding this research, in fact, have been: is it possible to provide a formal
representational framework which, for the same concept, combines both the classical
modelling view (accounting for compositional information) and defeasible, prototypical
knowledge ? Is it possible to propose a modelling architecture able to provide different
type of reasoning (e.g. classical deductive reasoning for the compositional component
and a non monotonic reasoning for the prototypical one)?
We suggest a possible answer to these questions proposing a modelling framework able
to represent, within the semantic web languages, a multilevel representation of
conceptual information, integrating both classical and non classical (typicality based)
information. Within this framework we hypothesise, at least in principle, the coexistence of multiple reasoning processes involving the different levels of
representation
Spatial location and its relevance for terminological inferences in bio-ontologies
<p>Abstract</p> <p>Background</p> <p>An adequate and expressive ontological representation of biological organisms and their parts requires formal reasoning mechanisms for their relations of physical aggregation and containment.</p> <p>Results</p> <p>We demonstrate that the proposed formalism allows to deal consistently with "role propagation along non-taxonomic hierarchies", a problem which had repeatedly been identified as an intricate reasoning problem in biomedical ontologies.</p> <p>Conclusion</p> <p>The proposed approach seems to be suitable for the redesign of compositional hierarchies in (bio)medical terminology systems which are embedded into the framework of the OBO (Open Biological Ontologies) Relation Ontology and are using knowledge representation languages developed by the Semantic Web community.</p
Semantic IoT Solutions - A Developer Perspective
Semantic technologies have recently gained significant support in a number of communities,
in particular the IoT community. An important problem to be solved is that, on the one hand,
it is clear that the value of IoT increases significantly with the availability of information from
a wide variety of domains. On the other hand, existing solutions target specific applications
or application domains and there is no easy way of sharing information between the
resulting silos. Thus, a solution is needed to enable interoperability across information silos.
As there is a huge heterogeneity regarding IoT technologies on the lower levels, the
semantic level is seen as a promising approach for achieving interoperability (i.e. semantic
interoperability) to unify IoT device description, data, bring common interaction, data
exploration, etc.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No.732240 (SynchroniCity) and No. 688467 (VICINITY); from ETSI under Specialist Task Forces 534, 556, 566 and 578. This work is partially funded by Hazards SEES NSF Award EAR 1520870, and KHealth NIH 1 R01 HD087132-01
Modélisation des signes dans les ontologies biomédicales pour l'aide au diagnostic.
Introduction : Établir un diagnostic mĂ©dical fiable requiert l identification de la maladie d un patient sur la base de l observation de ses signes et symptĂ´mes. Par ailleurs, les ontologies constituent un formalisme adĂ©quat et performant de reprĂ©sentation des connaissances biomĂ©dicales. Cependant, les ontologies classiques ne permettent pas de reprĂ©senter les connaissances liĂ©es au processus du diagnostic mĂ©dical : connaissances probabilistes et connaissances imprĂ©cises et vagues. MatĂ©riel et mĂ©thodes : Nous proposons des mĂ©thodes gĂ©nĂ©rales de reprĂ©sentation des connaissances afin de construire des ontologies adaptĂ©es au diagnostic mĂ©dical. Ces mĂ©thodes permettent de reprĂ©senter : (a) Les connaissances imprĂ©cises et vagues par la discrĂ©tisation des concepts (dĂ©finition de plusieurs catĂ©gories distinctes Ă l aide de valeurs seuils ou en reprĂ©sentant les diffĂ©rentes modalitĂ©s possibles). (b) Les connaissances probabilistes (les sensibilitĂ©s et les spĂ©cificitĂ©s des signes pour les maladies, et les prĂ©valences des maladies pour une population donnĂ©e) par la rĂ©ification des relations ayant des aritĂ©s supĂ©rieures Ă 2. (c) Les signes absents par des relations et (d) les connaissances liĂ©es au processus du diagnostic mĂ©dical par des règles SWRL. Un moteur d infĂ©rences abductif et probabiliste a Ă©tĂ© conçu et dĂ©veloppĂ©. Ces mĂ©thodes ont Ă©tĂ© testĂ©es Ă l aide de dossiers patients rĂ©els. RĂ©sultats : Ces mĂ©thodes ont Ă©tĂ© appliquĂ©es Ă trois domaines (les maladies plasmocytaires, les urgences odontologiques et les lĂ©sions traumatiques du genou) pour lesquels des modèles ontologiques ont Ă©tĂ© Ă©laborĂ©s. L Ă©valuation a permis de mesurer un taux moyen de 89,34% de rĂ©sultats corrects. Discussion-Conclusion : Ces mĂ©thodes permettent d avoir un modèle unique utilisable dans le cadre des raisonnements abductif et probabiliste, contrairement aux modèles proposĂ©s par : (a) Fenz qui n intègre que le mode de raisonnement probabiliste et (b) GarcĂa-crespo qui exprime les probabilitĂ©s hors du modèle ontologique. L utilisation d un tel système nĂ©cessitera au prĂ©alable son intĂ©gration dans le système d information hospitalier pour exploiter automatiquement les informations du dossier patient Ă©lectronique. Cette intĂ©gration pourrait ĂŞtre facilitĂ©e par l utilisation de l ontologie du système.Introduction: Making a reliable medical diagnosis requires the identification of the patient s disease based on the observation of signs. Moreover, ontologies provide an adequate and efficient formalism for medical knowledge representation. However, classical ontologies do not allow representing knowledge associated with medical reasoning such as probabilistic, imprecise, or vague knowledge. Material and methods: In the current work, general knowledge representation methods are proposed. They aim at building ontologies fitting to medical diagnosis. They allow to represent: (a) imprecise or vague knowledge by discretizing concepts (definition of several distinct categories thanks to threshold values or by representing the various possible modalities), (b) probabilistic knowledge (sensitivity, specificity and prevalence) by reification of relations of arity greater than 2, (c) absent signs by relations and (d) medical reasoning and reasoning on the absent signs by SWRL rules. An abductive reasoning engine and a probabilistic reasoning engine were designed and implemented. The methods were evaluated by use of real patient records. Results: These methods were applied to three domains (the plasma cell diseases, the dental emergencies and traumatic knee injuries) for which the ontological models were developed. The average rate of correct diagnosis was 89.34 %. Discussion-Conclusion: In contrast with other methods proposed by Fenz and GarcĂa-crespo, the proposed methods allow to have a unique model which can be used both for abductive and probabilistic reasoning. The use of such a system will require beforehand its integration in the hospital information system for the automatic exploitation of the electronic patient record. This integration might be made easier by the use of the ontology on which the system is based.RENNES1-Bibl. Ă©lectronique (352382106) / SudocSudocFranceF
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