L’organisation des connaissances est une discipline investie par des bibliothécaires, documentalistes, archivistes spécialistes de l’information, informaticiens et tous professionnels de documents. Elle englobe toutes activités, études et recherches qui élaborent et traitent les processus d’organisation et de présentation des ressources documentaires utiles dans une organisation. Dans ce contexte, le projet ANR Miipa-Doc a pour objectifs d’explorer des nouvelles méthodes d’indexation ascendantes, en utilisant des termes descripteurs formulés par les individus plutôt que choisis parmi une liste préétablie, pour l’organisation des contenus documentaires complexes au sein des entreprises de large taille, et concevoir l’architecture logicielle correspondante.Dans ce projet notre contribution consiste à gérer l’hétérogénéité d’un système d’information d’organisation des contenus documentaires, basé sur une approche orientée métier et un SOC (système d’organisation des connaissances) folksonomique à facette. Nous proposons dans cette gestion une approche incrémentale dirigée par les modèles, issue de l’IDM (ingénierie dirigée par les modèles), basée sur des méta-modèles pour garantir l’aspect d’évolutivité. Après l’implémentation du prototype HyperTaging qui met en place ces deux approches, nous proposons un processus d’évaluation permet de positionner ce prototype et tous SI de classification documentaire dans l’environnement des ECM, en se basant sur des critères d’évaluation fins et particuliers. / The knowledge organization is invested by librarians, archivists, information specialists, IT professionals and all discipline of document. It includes all activities, studies and research which develop and treat organization process and presentation of relevant information resources in an organization. In this context the Miipa-Doc project aims to explore new ascendants indexing methods, using descriptors made by individuals rather than selected given list for complex contained in the organization document, in large size companies, and design the corresponding software architecture.Our contribution in this project is to manage the heterogeneity of an information system of document organization, based on a business-oriented approach and a KOS (knowledge organization system) of folksonomy facet. We propose an incremental approach this management model driven, outcome of MDE (Model Driven Engineering), based on meta-models to ensure scalability appearance. After implementing the HyperTaging prototype, that implements both approaches, we propose an evaluation process used to position the prototype and all IS of documentary classification in the environment of ECM based on purposes of delicate and particular evaluation criteria.

Thesis discusses data mining the social media. It gives an introduction about the topic of data mining and possible mining methods. Thesis also explores social media and social networks, what are they able to offer and what problems do they bring. Three different APIs of three social networking sites are examined with their opportunities they provide for data mining. Techniques of text mining and document classification are explored. An implementation of a web application that mines data from social site Twitter using the algorithm SVM is being described. Implemented application is classifying tweets based on their text where classes represent tweets' continents of origin. Several experiments executed both in RapidMiner software and in implemented web application are then proposed and their results examined.

The classical search engine result page is used for many interactions with search results. While these are effective at communicating relevance, they do not present the context well. By giving the user an overview in the form of a spatialized display, in a domain that has a physical analog that the user is familiar with, context should become pre-attentive and obvious to the user. A prototype has been built that takes public medical information articles and assigns these to parts of the human body. The articles are indexed and made searchable. A visualization presents the coverage of a query on the human body and allows the user to interact with it to explore the results. Through usage cases the function and utility of the approach is shown.