28 research outputs found

    Gestion dynamique d'ontologies à partir de textes par systèmes multi-agents adaptatifs

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    Une ontologie est une représentation structurée des connaissances d'un domaine sous la forme d'un réseau conceptuel. Les ontologies sont considérées comme un support indispensable à la communication entre agents logiciels, à l'annotation des sites Web et des ressources documentaires dans une optique de recherche sémantique de l'information. Parce que les connaissances d'un domaine sont amenées à évoluer, une ontologie doit elle aussi évoluer pour rester en cohérence avec le domaine qu'elle modélise. Actuellement, la plupart des travaux traitant de l'évolution d'ontologies se préoccupent de la vérification et du maintien de la cohérence de l'ontologie modifiée. Ces travaux n'apportent pas de solutions concrètes à l'identification de nouvelles connaissances et à leur intégration dans une ontologie. Les travaux en ingénierie d'ontologies à partir de textes quant à eux traitent ce problème d'évolution comme un problème de reconstruction d'une nouvelle ontologie. Souvent, le résultat produit est complètement différent de l'ontologie à modifier. Par ailleurs, les logiciels d'évolution spécifiques à un domaine particulier rendent impossible leur utilisation dans d'autres domaines. Cette thèse propose une solution originale basée sur les systèmes multi-agents adaptatifs (AMAS) pour faire évoluer des ontologies à partir de textes. Chaque terme et concept sont représentés par un agent qui essaie de se situer au bon endroit dans l'organisation qui n'est autre que l'ontologie. Ce travail est concrétisé par un outil nommé DYNAMO. Un besoin d'évolution est déclenché par l'ajout de nouveaux textes dans un corpus de documents. DYNAMO utilise les résultats d'un extracteur de termes et de relations lexicales ainsi qu'un AMAS, nommé DYNAMO MAS, pour proposer une ontologie modifiée à un ontographe. Ce dernier interagit avec DYNAMO MAS via une interface graphique en modifiant l'ontologie proposée (déplacement, ajout, modification de concepts, de termes et/ou de relations), produisant ainsi des contraintes auxquelles l'AMAS doit s'adapter. Cette "coévolution" entre l'AMAS et l'ontographe cesse lorsque l'ontographe juge que l'ontologie modifiée est cohérente avec le nouveau corpus.An ontology is a structured representation of domain knowledge based on a conceptual network. Ontologies are considered as an essential support for the communication between software agents, the annotation of Web sites and textual resources to carry out semantic information retieval. Because domain knowledge can evolve, an ontology must also evolve to remain consistent with the domain that it models. Currently, studies on ontologies evolution are focusing on checking and maintaining the consistency of the evolved ontology. These works do not provide concrete solutions to the identification of new knowledge and its integration into an ontology. Ontology engineering from texts considers evolution as a problem of ontology reconstruction. The result produced by this kind of software is often completely different from the initial ontology. Moreover, it is almost impossible to reuse software designed only for a particular domain. This PhD thesis proposes an original solution based on adaptive multi-agent systems (AMAS) to evolve ontologies from texts. Each term and each concept are agentified and try to find its own right place in the AMAS organization that is the ontology. This work is implemented in a software called DYNAMO. An ontology evolution requirement is triggered by the addition of new texts in a corpus of documents. DYNAMO uses the results of a term extractor and a lexical relation extractor. These results are the input data of an AMAS, called DYNAMO MAS, that evolves an ontology and proposes it to an ontologist. Then, the ontologist interacts with DYNAMO MAS via a graphical interface by modifying the proposed ontology (moving, addition, suppression of concepts, terms and / or relationships). The ontologist's actions are feedback used by the AMAS to adapt the evolved ontology. This "coevolution" process between the AMAS and the ontologist ends when the ontologist judges that the modified ontology is consistent with the new corpus

    Giant cell reparative granuloma of the hallux following enchondroma

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    Giant cell reparative granuloma (GCRG) is a rare, benign intra osseous lytic lesion occurring especially in gnathis bone but also seen in feet and hands. It has similar clinical and radiological presentations than giant cell tumor, chondroblastoma, aneurysmal bone cyst, and hyperparathyroidism brown tumors but with specific histological findings We report a case of a GCRG of hallux phalanx in 18 years old patient appearing many years after enchondroma curettage and grafting. Radiographs showed a multiloculated osteolytic lesions involving whole phalanx with cortical thinning and without fluid-fluid levels in CT view. Expected to be an enchondroma recurrence, second biopsy confirmed diagnosis of GCRG with specific histological findings. Although if aetiopathogeny remains unknown, GCRG is reported to be a local non neoplasic reaction to an intraosseous hemorrhage. Our exceptional case claims that this tumor can appear in reaction to cellular disturbance primary or secondary

    Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

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    Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

    On the enrichment of a RDF Repository of City Points of Interest based on Social Data

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    International audiencePoints of interest (POIs) in a city are specific locations that present some significance to people; examples include restaurants, museums, hotels, theatres and landmarks, just to name a few. Due to their role in our social and economic life, POIs have been increasingly gaining the attention of location-based applications, such as on-line maps and social networking sites. While it is relatively easy to find on the Web basic information about a POI, such as its geographic location, telephone number and opening hours, it is more challenging to have a deeper knowledge as to what other people say about it. What if a person wants to know all the restaurants in parsi that serve good seafood and provide a kind service? Typically, the answer to this question has to be looked for on websites that let people leave comments and opinions on POIs, a time-consuming manual task that few are willing to do. This search would be better supported by search engines if information mined from opinions were available in a structured form, such as RDF. Inthis position paper, we describe a general approach to enrich an existing RDF repository about POIs with data obtained from social networking sites

    A Semantic Exploration Method Based on an Ontology of 17th Century Texts on Theatre: la Haine du Théâtre

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    International audienceThis paper proposes a method to explore a collection of texts with an ontology depending on a particular point of view. In the first part, the paper points out the characteristics of the corpus, composed of 17th century French texts. In the second part, it explains the methodology to isolate the discriminant terms for the ontology creation. Furthermore, not only the projection of the ontology on the texts is pointed out, but also how to explore the corpus thanks to the defined perspective based on semantic fields

    Meeting Intents Detection Based on Ontology for Automatic Email Answering

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    National audienceAutomatic email answering is a difficult AI problem that combines classification, natural language understanding and text generation techniques. We present an original approach and a tool based on an ontology to automatically reply to meeting emails. We constructed the ontology from a French corpus of 1150 emails in which the concepts represent detailed meeting intents (proposing a meeting, cancelling a meeting, rescheduling a meeting) and different answer templates. Each intent concept is a semantic rule formalized according to the FrameNet methodology. These rules are used to detect intents in emails and also to extract relevant information (such as date, time or person) used for generating replies. The main advantage of our approach is the generation of more precise answers than those proposed by other approaches. We tested the intent detection step on a set of 297 emails and compared it with different supervised machine learning algorithms. Obtained results are encouraging, with an accuracy 20% higher than results obtained with other algorithms. Mots-clés : Ontology engineering, knowledge acquisition from text, knowledge-based recommendation systems

    Gestion dynamique d'ontologies à partir de textes par systèmes multi-agents adaptatifs

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    TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF
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