Background: In recent years, several new ELISAs for the detection of antibodies against the porcine reproductive and respiratory disease virus (PRRSV) in pig serum have been developed. To interpret the results, specificity and sensitivity data as well as agreement to a reference ELISA must be available. In this study, three commercial ELISAs (INgezim PRRS 2.0 - ELISA II, Priocheck® PRRSV Ab porcine – ELISA III and CIVTEST suis PRRS E/S PLUS - ELISA IV, detecting PRRSV type 1 antibodies) were compared to a standard ELISA (IDEXX PRRS X3 Ab Test - ELISA I). The serum of three pigs vaccinated with an attenuated PRRSV live vaccine (genotype 2) was tested prior to and several times after the vaccination. Furthermore, serum samples of 245 pigs of PRRSV positive herds, 309 pigs of monitored PRRSV negative herds, 256 fatteners of assumed PRRSV negative herds with unknown herd history and 92 wild boars were tested with all four ELISAs. Results: ELISAs II and III were able to detect seroconversion of vaccinated pigs with a similar reliability. According to kappa coefficient, the results showed an almost perfect agreement between ELISA I as reference and ELISA II and III (kappa > 0.8), and substantial agreement between ELISA I and ELISA IV (kappa = 0.71). Sensitivity of ELISA II, III and IV was 96.0%, 100% and 91.5%, respectively. The specificity of the ELISAs determined in samples of monitored PRRSV negative herds was 99.0%, 95.1% and 96.4%, respectively. In assumed negative farms that were not continually monitored, more positive samples were found with ELISA II to IV. The reference ELISA I had a specificity of 100% in this study. Conclusions: All tested ELISAs were able to detect a PRRSV positive herd. The specificity and sensitivity of the tested commercial ELISAs, however, differed. ELISA II had the highest specificity an ELISA III had the highest sensitivity in comparison to the reference ELISA. ELISA IV had a lower sensitivity and specificity than the other ELISAs.

[pt] A abordagem de Dados Abertos tem como objetivo promover a interoperabilidade de dados na Web. Consiste na publicação de informações em formatos que permitam seu compartilhamento, descoberta, manipulação e acesso por parte de usuários e outros aplicativos de software. Essa abordagem requer a triplificação de conjuntos de dados, ou seja, a conversão do esquema de bases de dados relacionais, bem como suas instâncias, em triplas RDF. Uma questão fundamental neste processo é decidir a forma de representar conceitos de esquema de banco de dados em termos de classes e propriedades RDF. Isto é realizado através do mapeamento das entidades e relacionamentos para um ou mais vocabulários RDF, usados como base para a geração das triplas. A construção destes vocabulários é extremamente importante, porque quanto mais padrões são utilizados, melhor o grau de interoperabilidade com outros conjuntos de dados. No entanto, as ferramentas disponíveis atualmente não oferecem suporte adequado ao reuso de vocabulários RDF padrão no processo de triplificação. Neste trabalho, apresentamos o processo StdTrip, que guia usuários no processo de triplificação, promovendo o reuso de vocabulários de forma a assegurar interoperabilidade dentro do espaço da Linked Open Data (LOD). / [en] Open Data is a new approach to promote interoperability of data in the Web. It consists in the publication of information produced, archived and distributed by organizations in formats that allow it to be shared, discovered, accessed and easily manipulated by third party consumers. This approach requires the triplification of datasets, i.e., the conversion of database schemata and their instances to a set of RDF triples. A key issue in this process is deciding how to represent database schema concepts in terms of RDF classes and properties. This is done by mapping database concepts to an RDF vocabulary, used as the base for generating the triples. The construction of this vocabulary is extremely important, because the more standards are reused, the easier it will be to interlink the result to other existing datasets. However, tools available today do not support reuse of standard vocabularies in the triplification process, but rather create new vocabularies. In this thesis, we present the StdTrip process that guides users in the triplification process, while promoting the reuse of standard, RDF vocabularies.

The detection by serology of nematode-transmitted polyhedral viruses (nepoviruses) in grapevines is often unreliable. Nepoviruses were detected by enzyme-linked immunosorbent assay (ELISA) in tissue-cultured and field-grown grapevines. Nepovirus detection in in vitro plants (plantlets) was affected by virus distribution and growth room temperature. The reliability of virus detection in field-grown grapevines was improved when modified grinding buffers were used. Arabis mosaic virus (AMV) was detected by ELISA, for the first time, in in vitro grapevines initiated from field-and screenhouse-grown plants throughout the summer. The virus was not reliably and repeatedly detected in in vitro plantlets grown at 25°C. AMV and grapevine fanleaf virus (GFLV) distribution was not uniform throughout the plantlets. This distribution was affected by the culture room temperature. The best plant parts to sample for virus detection came from the zones of rapidly proliferating shoots. The viruses were sometimes not detected in samples taken from other tissues. Growth room temperature had an important effect on virus detection in plantlets. The highest virus titres were found in plants growing at 15°C. Temperature increases in 5°C steps to 30°C reduced virus titre. AMV became undetectable in nearly all plantlets growing at 30°C for as little as 30 days. Growth at 30°C reduced ELISA absorbance values by 76% after 8 days and after 21 days the values were at 4% of pre-treatment levels. The virus titre dropped below detectable levels in most plantlets. AMV could not be detected in plantlets or rooted explants after being placed in a 30°C treatment for 2 months. Tomato ringspot virus was detected by ELISA, for the first time, in in vitro grapevine plants. The virus was repeatedly detected in in vitro plants growing at 20°C. Under the typical summer conditions experienced at Sidney, B.C., modifying the standard ELISA grinding buffer (0.01 M phosphate buffered saline, pH 7.4, 0.05% Tween-20, 0.2% ovalbumin, 2% polyvinylpyrrolidone) was essential for reliable detection of AMV or GFLV. AMV was reliably detected by ELISA in foliar samples from field or screenhouse plants throughout the summer when the grinding buffer was modified by increasing the pH to at least 8.2 and adding 1% nicotine or 0.15 M phosphate buffered saline. The most reliable results with GFLV were obtained with the nicotine enhanced buffers. In comparison, because of the increased workload associated with growing plants in vitro and the unreliable detection of viruses in these plants, it remained preferable to detect nepoviruses in field plants by ELISA. / Land and Food Systems, Faculty of / Graduate

One the key feature behind Linked Data is the use of vocabularies that allow datasets to share a common language to describe similar concepts and relationships and resolve ambiguities between them. The development of vocabularies is often driven by a consensus process among datasets implementers, in which the criterion of interoperability is considered to be sufficient. This can lead to misrepresentation of real-world entities in Linked Data vocabularies entities. Such drawbacks can be fixed by the use of a formal methodology for modelling Linked Data vocabularies entities and identifying ontological distinctions. One proven example is the OntoClean methodology for curing taxonomies. In this work, it is presented a software tool that implements the PURO approach to ontological distinction modelling. PURO models vocabularies as Ontological Foreground Models (OFM), and the structure of ontological distinctions as Ontological Background Models (OBM), constructed using meta-properties attached to vocabulary entities, in a process known as vocabulary annotation. The software tool, named Background Annotation plugin, written in Java and integrated in the Protégé ontology editor, enables a user to graphically annotate vocabulary entities through an annotation workflow, that implements, among other things, persistency of annotations and their retrieval. Two kinds of workflows are supported: generic and dataset-specific, in order to differentiate a vocabulary usage, in terms of a PURO OBM, with respect to a given Linked Data dataset. The workflow is enhanced by the use of dataset statistical indicators retrieved through the Sindice service, for a sample of chosen datasets, such as the number of entities present in a dataset, and the relative frequency of vocabulary entities in that dataset. A further enhancement is provided by dataset summaries that offer an overview of the most common entity-property paths found in a dataset. Foreseen utilisation of the Background Annotation plugin include: 1) the checking of mapping agreement between different datasets, as produced by the R2R framework and 2) annotation of dependent resources in Concise Boundaries Descriptions of entities, used in data sampling from Linked Data datasets for data mining purposes.

In recent years, the Linked Data (LD) paradigm has emerged as a simple mechanism for employing the Web as a medium for data and knowledge integration where both documents and data are linked. Moreover, the semantics and structure of the underlying data are kept intact, making this the Semantic Web. LD essentially entails a set of best practices for publishing and connecting structure data on the Web, which allows publish- ing and exchanging information in an interoperable and reusable fashion. Many different communities on the Internet such as geographic, media, life sciences and government have already adopted these LD principles. This is confirmed by the dramatically growing Linked Data Web, where currently more than 50 billion facts are represented. With the emergence of Web of Linked Data, there are several use cases, which are possible due to the rich and disparate data integrated into one global information space. Linked Data, in these cases, not only assists in building mashups by interlinking heterogeneous and dispersed data from multiple sources but also empowers the uncovering of meaningful and impactful relationships. These discoveries have paved the way for scientists to explore the existing data and uncover meaningful outcomes that they might not have been aware of previously. In all these use cases utilizing LD, one crippling problem is the underlying data quality. Incomplete, inconsistent or inaccurate data affects the end results gravely, thus making them unreliable. Data quality is commonly conceived as fitness for use, be it for a certain application or use case. There are cases when datasets that contain quality problems, are useful for certain applications, thus depending on the use case at hand. Thus, LD consumption has to deal with the problem of getting the data into a state in which it can be exploited for real use cases. The insufficient data quality can be caused either by the LD publication process or is intrinsic to the data source itself. A key challenge is to assess the quality of datasets published on the Web and make this quality information explicit. Assessing data quality is particularly a challenge in LD as the underlying data stems from a set of multiple, autonomous and evolving data sources. Moreover, the dynamic nature of LD makes assessing the quality crucial to measure the accuracy of representing the real-world data. On the document Web, data quality can only be indirectly or vaguely defined, but there is a requirement for more concrete and measurable data quality metrics for LD. Such data quality metrics include correctness of facts wrt. the real-world, adequacy of semantic representation, quality of interlinks, interoperability, timeliness or consistency with regard to implicit information. Even though data quality is an important concept in LD, there are few methodologies proposed to assess the quality of these datasets. Thus, in this thesis, we first unify 18 data quality dimensions and provide a total of 69 metrics for assessment of LD. The first methodology includes the employment of LD experts for the assessment. This assessment is performed with the help of the TripleCheckMate tool, which was developed specifically to assist LD experts for assessing the quality of a dataset, in this case DBpedia. The second methodology is a semi-automatic process, in which the first phase involves the detection of common quality problems by the automatic creation of an extended schema for DBpedia. The second phase involves the manual verification of the generated schema axioms. Thereafter, we employ the wisdom of the crowds i.e. workers for online crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) to assess the quality of DBpedia. We then compare the two approaches (previous assessment by LD experts and assessment by MTurk workers in this study) in order to measure the feasibility of each type of the user-driven data quality assessment methodology. Additionally, we evaluate another semi-automated methodology for LD quality assessment, which also involves human judgement. In this semi-automated methodology, selected metrics are formally defined and implemented as part of a tool, namely R2RLint. The user is not only provided the results of the assessment but also specific entities that cause the errors, which help users understand the quality issues and thus can fix them. Finally, we take into account a domain-specific use case that consumes LD and leverages on data quality. In particular, we identify four LD sources, assess their quality using the R2RLint tool and then utilize them in building the Health Economic Research (HER) Observatory. We show the advantages of this semi-automated assessment over the other types of quality assessment methodologies discussed earlier. The Observatory aims at evaluating the impact of research development on the economic and healthcare performance of each country per year. We illustrate the usefulness of LD in this use case and the importance of quality assessment for any data analysis.

Over the past decade, the public awareness and availability as well as methods for the creation and use of spatial data on the Web have steadily increased. Besides the establishment of governmental Spatial Data Infrastructures (SDIs), numerous volunteered and commercial initiatives had a major impact on that development. Nevertheless, data isolation still poses a major challenge. Whereas the majority of approaches focuses on data provision, means to dynamically link and combine spatial data from distributed, often heterogeneous data sources in an ad hoc manner are still very limited. However, such capabilities are essential to support and enhance information retrieval for comprehensive spatial decision making. To facilitate spatial data fusion in current SDIs, this thesis has two main objectives. First, it focuses on the conceptualization of a service-based fusion process to functionally extend current SDI and to allow for the combination of spatial data from different spatial data services. It mainly addresses the decomposition of the fusion process into well-defined and reusable functional building blocks and their implementation as services, which can be used to dynamically compose meaningful application-specific processing workflows. Moreover, geoprocessing patterns, i.e. service chains that are commonly used to solve certain fusion subtasks, are designed to simplify and automate workflow composition. Second, the thesis deals with the determination, description and exploitation of spatial data relations, which play a decisive role for spatial data fusion. The approach adopted is based on the Linked Data paradigm and therefore bridges SDI and Semantic Web developments. Whereas the original spatial data remains within SDI structures, relations between those sources can be used to infer spatial information by means of Semantic Web standards and software tools. A number of use cases were developed, implemented and evaluated to underpin the proposed concepts. Particular emphasis was put on the use of established open standards to realize an interoperable, transparent and extensible spatial data fusion process and to support the formalized description of spatial data relations. The developed software, which is based on a modular architecture, is available online as open source. It allows for the development and seamless integration of new functionality as well as the use of external data and processing services during workflow composition on the Web. / Die Entwicklung des Internet im Laufe des letzten Jahrzehnts hat die Verfügbarkeit und öffentliche Wahrnehmung von Geodaten, sowie Möglichkeiten zu deren Erfassung und Nutzung, wesentlich verbessert. Dies liegt sowohl an der Etablierung amtlicher Geodateninfrastrukturen (GDI), als auch an der steigenden Anzahl Communitybasierter und kommerzieller Angebote. Da der Fokus zumeist auf der Bereitstellung von Geodaten liegt, gibt es jedoch kaum Möglichkeiten die Menge an, über das Internet verteilten, Datensätzen ad hoc zu verlinken und zusammenzuführen, was mitunter zur Isolation von Geodatenbeständen führt. Möglichkeiten zu deren Fusion sind allerdings essentiell, um Informationen zur Entscheidungsunterstützung in Bezug auf raum-zeitliche Fragestellungen zu extrahieren. Um eine ad hoc Fusion von Geodaten im Internet zu ermöglichen, behandelt diese Arbeit zwei Themenschwerpunkte. Zunächst wird eine dienstebasierten Umsetzung des Fusionsprozesses konzipiert, um bestehende GDI funktional zu erweitern. Dafür werden wohldefinierte, wiederverwendbare Funktionsblöcke beschrieben und über standardisierte Diensteschnittstellen bereitgestellt. Dies ermöglicht eine dynamische Komposition anwendungsbezogener Fusionsprozesse über das Internet. Des weiteren werden Geoprozessierungspatterns definiert, um populäre und häufig eingesetzte Diensteketten zur Bewältigung bestimmter Teilaufgaben der Geodatenfusion zu beschreiben und die Komposition und Automatisierung von Fusionsprozessen zu vereinfachen. Als zweiten Schwerpunkt beschäftigt sich die Arbeit mit der Frage, wie Relationen zwischen Geodatenbeständen im Internet erstellt, beschrieben und genutzt werden können. Der gewählte Ansatz basiert auf Linked Data Prinzipien und schlägt eine Brücke zwischen diensteorientierten GDI und dem Semantic Web. Während somit Geodaten in bestehenden GDI verbleiben, können Werkzeuge und Standards des Semantic Web genutzt werden, um Informationen aus den ermittelten Geodatenrelationen abzuleiten. Zur Überprüfung der entwickelten Konzepte wurde eine Reihe von Anwendungsfällen konzipiert und mit Hilfe einer prototypischen Implementierung umgesetzt und anschließend evaluiert. Der Schwerpunkt lag dabei auf einer interoperablen, transparenten und erweiterbaren Umsetzung dienstebasierter Fusionsprozesse, sowie einer formalisierten Beschreibung von Datenrelationen, unter Nutzung offener und etablierter Standards. Die Software folgt einer modularen Struktur und ist als Open Source frei verfügbar. Sie erlaubt sowohl die Entwicklung neuer Funktionalität durch Entwickler als auch die Einbindung existierender Daten- und Prozessierungsdienste während der Komposition eines Fusionsprozesses.

Over the last years, the Linked Open Data (LOD) has evolved from a mere 12 to more than 10,000 knowledge bases. These knowledge bases come from diverse domains including (but not limited to) publications, life sciences, social networking, government, media, linguistics. Moreover, the LOD cloud also contains a large number of crossdomain knowledge bases such as DBpedia and Yago2. These knowledge bases are commonly managed in a decentralized fashion and contain partly verlapping information. This architectural choice has led to knowledge pertaining to the same domain being published by independent entities in the LOD cloud. For example, information on drugs can be found in Diseasome as well as DBpedia and Drugbank. Furthermore, certain knowledge bases such as DBLP have been published by several bodies, which in turn has lead to duplicated content in the LOD . In addition, large amounts of geo-spatial information have been made available with the growth of heterogeneous Web of Data. The concurrent publication of knowledge bases containing related information promises to become a phenomenon of increasing importance with the growth of the number of independent data providers. Enabling the joint use of the knowledge bases published by these providers for tasks such as federated queries, cross-ontology question answering and data integration is most commonly tackled by creating links between the resources described within these knowledge bases. Within this thesis, we spur the transition from isolated knowledge bases to enriched Linked Data sets where information can be easily integrated and processed. To achieve this goal, we provide concepts, approaches and use cases that facilitate the integration and enrichment of information with other data types that are already present on the Linked Data Web with a focus on geo-spatial data. The first challenge that motivates our work is the lack of measures that use the geographic data for linking geo-spatial knowledge bases. This is partly due to the geo-spatial resources being described by the means of vector geometry. In particular, discrepancies in granularity and error measurements across knowledge bases render the selection of appropriate distance measures for geo-spatial resources difficult. We address this challenge by evaluating existing literature for point set measures that can be used to measure the similarity of vector geometries. Then, we present and evaluate the ten measures that we derived from the literature on samples of three real knowledge bases. The second challenge we address in this thesis is the lack of automatic Link Discovery (LD) approaches capable of dealing with geospatial knowledge bases with missing and erroneous data. To this end, we present Colibri, an unsupervised approach that allows discovering links between knowledge bases while improving the quality of the instance data in these knowledge bases. A Colibri iteration begins by generating links between knowledge bases. Then, the approach makes use of these links to detect resources with probably erroneous or missing information. This erroneous or missing information detected by the approach is finally corrected or added. The third challenge we address is the lack of scalable LD approaches for tackling big geo-spatial knowledge bases. Thus, we present Deterministic Particle-Swarm Optimization (DPSO), a novel load balancing technique for LD on parallel hardware based on particle-swarm optimization. We combine this approach with the Orchid algorithm for geo-spatial linking and evaluate it on real and artificial data sets. The lack of approaches for automatic updating of links of an evolving knowledge base is our fourth challenge. This challenge is addressed in this thesis by the Wombat algorithm. Wombat is a novel approach for the discovery of links between knowledge bases that relies exclusively on positive examples. Wombat is based on generalisation via an upward refinement operator to traverse the space of Link Specifications (LS). We study the theoretical characteristics of Wombat and evaluate it on different benchmark data sets. The last challenge addressed herein is the lack of automatic approaches for geo-spatial knowledge base enrichment. Thus, we propose Deer, a supervised learning approach based on a refinement operator for enriching Resource Description Framework (RDF) data sets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples. Each of the proposed approaches is implemented and evaluated against state-of-the-art approaches on real and/or artificial data sets. Moreover, all approaches are peer-reviewed and published in a conference or a journal paper. Throughout this thesis, we detail the ideas, implementation and the evaluation of each of the approaches. Moreover, we discuss each approach and present lessons learned. Finally, we conclude this thesis by presenting a set of possible future extensions and use cases for each of the proposed approaches.

Die vorliegende Masterarbeit dokumentiert die Erstellung eines RDF-basierten Vokabulars zur Darstellung von Nutzungsstatistiken elektronischer Ressourcen, die nach dem COUNTER-Standard erstellt wurden. Die konkrete Anwendung dieses Vokabulars bildet das Electronic Resource Management System (ERMS), welches momentan von der Universitätsbibliothek Leipzig im Rahmen des kooperativen Projektes AMSL entwickelt wird. Dieses basiert auf Linked Data, soll die veränderten Verwaltungsprozesse elektronischer Ressourcen abbilden können und gleichzeitig anbieterunabhängig und flexibel sein. Das COUNTER-Vokabular soll aber auch über diese Anwendung hinaus einsetzbar sein. Die Arbeit gliedert sich in die beiden Teile Grundlagen und Modellierung. Im ersten Teil wird zu nächst die bibliothekarische Notwendigkeit von ERM-Systemen herausgestellt und der Fokus der Betrachtung auf das Teilgebiet der Nutzungsstatistiken und die COUNTER-Standardisierung gelenkt. Anschließend werden die technischen Grundlagen der Modellierung betrachtet, um die Arbeit auch für nicht mit Linked Data vertraute Leser verständlich zu machen. Darauf folgt der Modellierungsteil, der mit einer Anforderungsanalyse sowie der Analyse des den COUNTER-Dateien zugrunde liegenden XML-Schemas beginnt. Daran schließt sich die Modellierung des Vokabulars mit Hilfe von RDFS und OWL an. Aufbauend auf angestellten Überlegungen zur Übertragung von XML-Statistiken nach RDF und der Vergabe von URIs werden anschließend reale Beispieldateien manuell konvertiert und in einem kurzen Test erfolgreich überprüft. Den Abschluss bilden ein Fazit der Arbeit sowie ein Ausblick auf das weitere Verfahren mit den Ergebnissen. Das erstellte RDF-Vokabular ist bei GitHub unter der folgenden URL zur Weiterverwendung hinterlegt: https://github.com/a-nnika/counter.vocab:Inhaltsverzeichnis Abbildungsverzeichnis 6 Tabellenverzeichnis 7 Abkürzungsverzeichnis 8 1 Einleitung 9 1.1 Problematik, Ziel und Abgrenzung 9 1.2 Zielgruppe, Methodik und Aufbau 11 1.3 Forschungsstand und Quellenlage 13 TEIL I - Grundlagen 17 2 Bibliothekarische Ausgangssituation 18 2.1 Electronic Resource Management 18 2.2 Nutzungsdaten elektronischer Ressourcen 20 2.3 Projekt AMSL 23 3 Technischer Hintergrund 26 3.1 XML 26 3.2 Linked Data und Semantic Web 27 3.3 Grundkonzepte der Modellierung 29 3.4 RDF 30 3.4.1 Datenmodell 30 3.4.2 Serialisierungen 34 3.5 RDFS 36 3.6 OWL 38 TEIL II - Modellierung 41 4 Vorarbeiten 42 4.1 Anforderungsanalyse 42 4.2 Analyse des COUNTER XML-Schemas 45 4.2.1 Grundstruktur 45 4.2.2 Details 48 4.3 Grundkonzeption 54 4.4 Verwendete Programme 56 4.4.1 Notepad++ 56 4.4.2 Raptor 58 4.4.3 OntoWiki 59 5 Realisierung des RDF-Vokabulars 61 5.1 Grundlegende Modellierung: RDFS 61 5.2 Erweiterung: OWL 70 5.3 Übertragung von XML-Daten nach RDF 75 5.4 URI-Vergabe 78 6 Test des Vokabulars 83 6.1 Planung des Tests 83 6.2 Erstellung von Testdatensätzen 85 6.3 Testergebnisse 87 7 Fazit und Ausblick 90 Literatur- und Quellenverzeichnis 93 Selbstständigkeitserklärung 101 Anhänge I

The study focuses on tariff-linked (or commodity-linked) agreements entered into between a power utility and commodity producers. The main purpose of these types of agreements is to link electricity tariff payable by commodity producers to the price of the commodity produced thereby transferring a certain level of commodity price risk to the power utility. The study looks at risk management practices of a power utility company with a particular reference to tariff-linked agreements. Also, the study critically analyses risk hedging mechanisms put in place by the power utility. The report makes practical recommendations, where applicable, in dealing with these risks. Risk management continuously evolve to meet the challenges of complex financial world. Despite the latest sophisticated risk management tools available commodity producers still encounter difficulties to hedge the price risk. The challenge for the power utility is the application of new risk management tools to effectively manage price risk. / Business Management / M.Com. (Business Economics)

Cette thèse présente un système permettant d’extraire les interactions partagées dans les réseaux sociaux et de construire un profil dynamique d’expertise pour chaque membre dudit réseau social. La difficulté principale dans cette partie est l’analyse de ces interactions, souvent très courtes et avec peu de structure grammaticale et linguistique. L’approche que nous avons mis en place propose de relier les termes importants de ces messages à des concepts dans une base de connaissance sémantique, type Linked Data. Cette connexion permet en effet d’enrichir le champ sémantique des messages en exploitant le voisinage sémantique du concept dans la base de connaissances. Notre première contribution dans ce contexte est un algorithme qui permet d'effectuer cette liaison avec une précision plus augmentée par rapport à l’état de l’art, en considérant le profil de l’utilisateur ainsi que les messages partagés dans la communauté dont il est membre comme source supplémentaire de contexte. La deuxième étape de l’analyse consiste à effectuer l’expansion sémantique du concept en exploitant les liens dans la base de connaissance. Notre algorithme utilise une heuristique basant sur le calcul de similarité entre les descriptions des concepts pour ne garder que ceux les plus pertinents par rapport au profil de l’utilisateur. Les deux algorithmes mentionnés précédemment permettent d’avoir un ensemble de concepts qui illustrent les centres d'expertise de l’utilisateur. Afin de mesurer le degré d'expertise de l’utilisateur qui s’applique sur chaque concept dans son profil, nous appliquons la méthode-standard vectoriel et associons à chaque concept une mesure composée de trois éléments : (i) le tf-idf, (ii) le sentiment moyen que l’utilisateur exprime par rapport au dit concept et (iii) l’entropie moyen des messages partagés contenant ledit concept. L’ensemble des trois mesures combinées permet d’avoir un poids unique associé à chaque concept du profil. Ce modèle de profil vectoriel permet de trouver les « top-k » profils les plus pertinents par rapport à une requête. Afin de propager ces poids sur les concepts dans l’expansion sémantique, nous avons appliqué un algorithme de type propagation sous contrainte (Constrained Spreading Activation), spécialement adapté à la structure d'un graphe sémantique. L’application réalisée pour prouver l’efficacité de notre approche, ainsi que d’illustrer la stratégie de recommandation est un système disponible en ligne, nommé « The Tagging Beak » (http://www.tbeak.com). Nous avons en effet développé une stratégie de recommandation type Q&A (question - réponse), où les utilisateurs peuvent poser des questions en langage naturel et le système recommande des personnes à contacter ou à qui se connecter pour être notifié de nouveaux messages pertinents par rapport au sujet de la question / In recent years, online collaborative environments, e.g. social content sites (such as Twitter or Facebook) have significantly changed the way people share information and interact with peers. These platforms have become the primary common environment for people to communicate about their activity and their information needs and to maintain and create social ties. Status updates or microposts emerged as a convenient way for people to share content frequently without a long investment of time. Some social platforms even limit the length of a “post”. A post generally consists of a single sentence (e.g. news, a question), it can include a picture, a hyperlink, tags or other descriptive data (metadata). Contrarily to traditional documents, posts are informal (with no controlled vocabulary) and don't have a well established structure. Social platforms can become so popular (huge number of users and posts), that it becomes difficult to find relevant information in the flow of notifications. Therefore, organizing this huge quantity of social information is one of the major challenges of such collaborative environments. Traditional information retrieval techniques are not well suited for querying such corpus, because of the short size of the share content, the uncontrolled vocabulary used by author and because these techniques don't take in consideration the ties in-between people. Also, such techniques tend to find the documents that best match a query, which may not be sufficient in the context of social platform where the creation of new connections in the platform has a motivating impact and where the platform tries to keep on-going participation. A new information retrieval paradigm, social search has been introduced as a potential solution to this problem. This solution consists of different strategies to leverage user generated content for information seeking, such as the recommendation of people. However, existing strategies have limitations in the user profile construction process and in the routing of queries to the right people identified as experts. More concretely, the majority of user profiles in such systems are keyword-based, which is not suited for the small size and the informal aspect of the posts. Secondly, expertise is measured only based on statistical scoring mechanisms, which do not take into account the fact that people on social platforms will not precisely consume the results of the query, but will aim to engage into a conversation with the expert. Also a particular focus needs to be done on privacy management, where still traditional methods initially designed for databases are used without taking into account the social ties between people. In this thesis we propose and evaluate an original framework for the organization and retrieval of information in social platforms. Instead of retrieving content that best matches a user query, we retrieve people who have expertise and are most motivated to engage in conversations on its topics. We propose to build dynamically profiles for users based on their interactions in the social platform. The construction of such profiles requires the capture of interactions (microposts), their analysis and the extraction and understanding of their topics. In order to build a more meaningful profile, we leverage Semantic Web Technologies and more specifically, Linked Data, for the transformation of microposts topics into semantic concepts. Our thesis contributes to several fields related to the organization, management and retrieval of information in collaborative environments and to the fields of social computing and human-computer interaction