Un service Web est un moyen d'offrir des fonctionnalités sur un réseau en utilisant des normes ouvertes pour la description et l'invocation. Les services Web représentent une réalisation importante de l'Architecture Orientée Service (AOS), à l'aide de qui, les applications peuvent être développées rapidement avec un coût bas par couplage faible les services sur un réseau. Cela nécessite la découverte et la composition des ensembles de services interopérables, selon certaines exigences fonctionnelles et non fonctionnelles. Les services Web confrontent de nombreux défis quant à leur découverte et sélection, en raison de plusieurs facteurs tels que: le nombre important de services, le manque de registres publics capables d'offrir des mécanismes efficaces de récupération de service, de leur nature dynamique qui impose divers aspects de QoS tels que la disponibilité, le temps de réponse, etc, et le manque de sémantique dans leurs descriptions d'interface. Dans cette thèse, nous avons deux objectifs principaux. Notre premier objectif est de faciliter la sélection des services Web et d'assurer la continuité du service dans des compositions de services Web. Par conséquent, nous proposons une approche basée sur l'analyse formelle de concepts (AFC) pour classer les services Web dans un premier temps par mots clés, puis par les valeurs de similarité entre leurs opérations. Cette classification est représentée comme un treillis de concepts qui révèle les relations entre les services, ce qui facilite la sélection d'un service nécessaire ainsi que l'identification des potentiels sauvegardes (substitutions en cas de panne). Notre deuxième objectif est de guider l'utilisateur en effectuant une sélection optimisée basée sur des plusieurs critères. Nous définissons un descripteur pour l'utilisateur qui spécifie des exigences fonctionnelles et non fonctionnelles. Dans ce descripteur, les propriétés fonctionnelles sont spécifiées comme un ensemble de mots-clés. Les propriétés non fonctionnelles représentent les niveaux attendus de QoS (bon, mauvais, moyen, ..) ainsi que la composition de services exprimée en tant que liens entre les propriétés fonctionnelles spécifiées. Afin d'atteindre cet objectif, nous proposons une approche basée sur l'analyse relationnelle de concepts (ARC) qui classifie les services Web en treillis de concepts similaires à la AFC, mais enrichis avec les propriétés non-fonctionnelles. Nous proposons également un mécanisme permettant d'interroger le concept de treillis résultant basée sur RCA, selon les exigences spécifiées dans le descripteur. Nous avons validé notre proposition en utilisant des services Web réels extraits de Service-Finder et Seekda (des moteurs de recherche de services Web). Pour l'approche basée sur la AFC, nous avons récupéré un total de 145 services Web que nous avons classés en fonction de leur fonctionnalité. Nous avons montré comment sélectionner efficacement un service offrant les fonctionnalités requises et la manière d'identifier ses sauvegardes. Pour l'approche basée sur RCA, nous avons récupéré 901 services Web que nous avons classés selon leur niveau de QoS et de composabilité. Nous avons vérifié que cette approche permet une sélection efficace des services correspondant aux exigences fonctionnelles et non fonctionnelles spécifiées. / A Web service is a way of offering functionality over a network using open standards for description and invocation. Web services represent an important realization of Service-Oriented Architectures (SOA), using which, applications can be developed rapidly with a low cost by loosely coupling services over a network. This necessitates discovering and composing sets of interoperable services, according to some functional and non-functional requirements.Web services face many challenges regarding their discovery and selection, due to several factors like: the fairly large number of services, the lack of public registries capable of offering efficient service retrieval mechanisms, their dynamic nature which imposes various QoS aspects such as availability, response time, etc., and the lack of semantics in their interface descriptions.In this thesis, we have two main objectives. Our first objective is to facilitate Web service selection and assure service continuity in Web service compositions. Therefore, we propose an approach based on Formal Concept Analysis (FCA) to classify Web services first by keywords then by similarity values between their operations. This classification is represented as a concept lattice that reveals the relations between the services, which facilitates the selection of a needed service as well as the identification of its potential backups (substitution in case of failure).Our second objective is to guide the user towards performing an optimized multi-criteria based selection. We define a user requirements descriptor that specifies the needed functional and non-functional properties. Inside this descriptor, functional properties are specified as a set of keywords. Non-functional properties represent the expected QoS levels (good, bad, medium, ..) as well as the composition of services expressed as links between the specified functional properties. In order to meet this objective, we propose an approach based on Relational Concept Analysis (RCA) that classifies Web services into concept lattices similar to FCA, but enriched with the non-functional properties. We also propose a mechanism to query the resulting RCA-based concept lattices, according to the requirements specified in the descriptor.We validated our proposition using real Web services retrieved from Service-Finder and Seekda Web service search engines. For the FCA-based approach, we retrieved a total of 145 Web services that we classified by their functionality. We showed how to select efficiently a service offering the required functionality and how to identify its backups.For the RCA-based approach, we retrieved 901 Web services that we classified by their QoS and composability levels. We verified that the approach allows an efficient selection of services corresponding to the specified functional and non-functional requirements.

Description Logic (abbrv. DL) belongs to the field of knowledge representation and reasoning. DL researchers have developed a large family of logic-based languages, so-called description logics (abbrv. DLs). These logics allow their users to explicitly represent knowledge as ontologies, which are finite sets of (human- and machine-readable) axioms, and provide them with automated inference services to derive implicit knowledge. The landscape of decidability and computational complexity of common reasoning tasks for various description logics has been explored in large parts: there is always a trade-off between expressibility and reasoning costs. It is therefore not surprising that DLs are nowadays applied in a large variety of domains: agriculture, astronomy, biology, defense, education, energy management, geography, geoscience, medicine, oceanography, and oil and gas. Furthermore, the most notable success of DLs is that these constitute the logical underpinning of the Web Ontology Language (abbrv. OWL) in the Semantic Web. Formal Concept Analysis (abbrv. FCA) is a subfield of lattice theory that allows to analyze data-sets that can be represented as formal contexts. Put simply, such a formal context binds a set of objects to a set of attributes by specifying which objects have which attributes. There are two major techniques that can be applied in various ways for purposes of conceptual clustering, data mining, machine learning, knowledge management, knowledge visualization, etc. On the one hand, it is possible to describe the hierarchical structure of such a data-set in form of a formal concept lattice. On the other hand, the theory of implications (dependencies between attributes) valid in a given formal context can be axiomatized in a sound and complete manner by the so-called canonical base, which furthermore contains a minimal number of implications w.r.t. the properties of soundness and completeness. In spite of the different notions used in FCA and in DLs, there has been a very fruitful interaction between these two research areas. My thesis continues this line of research and, more specifically, I will describe how methods from FCA can be used to support the automatic construction and extension of DL ontologies from data.

We compute the expected numbers of both formal concepts and proper premises in a formal context that is chosen uniformly at random among all formal contexts of given dimensions.

Constructing description logic ontologies is a difficult task that is normally conducted by experts. Recent results show that parts of ontologies can be constructed from description logic interpretations. However, these results assume the interpretations to be free of errors, which may not be the case for real-world data. To provide some mechanism to handle these errors, the notion of confidence from data mining is introduced into description logics, yielding confident general concept inclusions (confident GCIs) of finite interpretations. The main focus of this work is to prove the existence of finite bases of confident GCIs and to describe some of theses bases explicitly.

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Yes / The analysis of potentially large volumes of crowd-sourced and social media data is central to meeting the requirements of the ATHENA project. Here, we discuss the various stages of the pipeline process we have developed, including acquisition of the data, analysis, aggregation, filtering, and structuring. We highlight the challenges involved when working with unstructured, noisy data from sources such as Twitter, and describe the crisis taxonomies that have been developed to support the tasks and enable concept extraction. State-of-the-art techniques such as formal concept analysis and machine learning are used to create a range of capabilities including concept drill down, sentiment analysis, credibility assessment, and assignment of priority. We ground many of these techniques using results obtained from a set of tweets which emerged from the Colorado wildfires of 2012 in order to demonstrate the applicability of our work to real crisis scenarios.

Implication is a logical connective corresponding to the rule of causality "if ... then ...". Implications allow one to organize knowledge of some field of application in an intuitive and convenient manner. This thesis explores possibilities of automatic construction of all valid implications (implicative theory) in a given field. As the main method for constructing implicative theories a robust active learning technique called Attribute Exploration was used. Attribute Exploration extracts knowledge from existing data and offers a possibility of refining this knowledge via providing counter-examples. In frames of the project implicative theories were constructed automatically for two mathematical domains: algebraic identities and parametrically expressible functions. This goal was achieved thanks both pragmatical approach of Attribute Exploration and discoveries in respective fields of application. The two diverse application fields favourably illustrate different possible usage patterns of Attribute Exploration for automatic construction of implicative theories.