With the growth of information stored over Internet, especially in the biological field, and with discoveries being made daily in this domain, scientists are faced with an overwhelming amount of articles. Reading all published articles is a tedious and time-consuming process. Therefore a way to summarise the information in the articles is needed. A solution is the derivation of an ontology representing the knowledge enclosed in the set of articles and allowing to browse through them. In this thesis we present the tool Ontolo, which allows to build an initial ontology of a domain by inserting a set of articles related to that domain in the system. The quality of the ontology construction has been tested by comparing our ontology results for keywords to the ones provided by the Gene Ontology for the same keywords. The obtained results are quite promising for a first prototype of the system as it finds many common terms on both ontologies for justa few hundred of inserted articles.

Les travaux présentés dans cette thèse proposent différentes méthodes, réflexions et réalisations associant Web 2.0 et Web Sémantique. Après avoir introduit ces deux notions, nous présentons les limites actuelles de certains outils, comme les blogs ou les wikis, et des pratiques de tagging dans un contexte d’Entreprise 2.0. Nous proposons ensuite la méthode SemSLATES et la vision globale d’une architecture de médiation reposant sur les standards du Web Sémantique (langages, modèles, outils et protocoles) pour pallier à ces limites. Nous détaillons par la suite différentes ontologies (au sens informatique) développées pour mener à bien cette vision : d’une part, en contribuant activement au projet SIOC - Semantically-Interlinked Online Communities -, des modèles destinés aux méta-données socio-structurelles, d’autre part des modèles, étendant des ontologies publiques, destinés aux données métier. De plus, la définition de l’ontologie MOAT - Meaning Of A Tag - nous permet de coupler la souplesse du tagging et la puissance de l'indexation à base d’ontologies. Nous revenons ensuite sur différentes implémentations logicielles que nous avons mises en place à EDF R&D pour permettre de manière intuitive la production et l'utilisation d'annotations sémantiques afin d’enrichir les outils initiaux : wikis sémantiques, interfaces avancées de visualisation (navigation à facettes, mash-up sémantique, etc.) et moteur de recherche sémantique. Plusieurs contributions ont été publiées sous forme d'ontologies publiques ou de logiciels libres, contribuant de manière plus large à cette convergence entre Web 2.0 et Web Sémantique non seulement en entreprise mais sur le Web dans son ensemble. / The work described in this thesis provides different methods, thoughts and implementations combining Web 2.0 and the Semantic Web. After introducing those terms, we present the current shortcomings of tools such as blogs and wikis as well as tagging practices in an Enterprise 2.0 context. We define the SemSLATES methodology and the global vision of a middleware architecture based on Semantic Web technologies (languages, models, tools and protocols) to solve these issues. Then, we detail the various ontologies (as in computer science) that we build to achieve this goal: on the one hand models dedicated to socio-structural meta-data, by actively contributing to SIOC - Semantically-Interlinked Online Communities -, and on the other hands models extending public ontologies for domain data. Moreover, the MOAT ontology - Meaning Of A Tag – allows us to combine the flexibility of tagging and the power of ontology-based indexing. We then describe several software implementations, at EDF R&D, dedicated to easily produce and use semantic annotations to enrich original tools: semantic wikis, advanced visualization interfaces (faceted browsing, semantic mash-ups, etc.) and a semantic search engine. Several contributions have been published as public ontologies or open-source software, contributing more generally to this convergence between Web 2.0 and the Semantic Web, not only in enterprise but on the Web as a whole.

Ever increasing complexity of software systems, and distributed and dynamic nature of today’s enterprise level computing have initiated the demand for more self aware, flexible and robust systems, where human beings could delegate much of their work to software agents. The Semantic Web presents new opportunities for enabling, modeling, sharing and reasoning with knowledge available on the web. These opportunities are made possible through the formal representation of knowledge domains with ontologies. Semantic Web is a vision of World Wide Web (WWW) level knowledge representation system where each piece of information is equipped with well defined meaning; enabling software agents to understand and process that information. This, in turn, enables people and software agents to work in a more smooth and collaborative way. In this thesis we have first presented a detailed overview of Semantic web vision by describing its fundamental building blocks which constitutes famous layered architecture of Semantic Web. We have discussed the mile stones Semantic Web vision has achieved so far in the areas of research, education and industry and on the other hand we have presented some of the social, business and technological barriers in the way of this vision to become reality. We have also evaluated that how Semantic vision is effecting some of the current technological and research areas like Web Services, Software Agents, Knowledge Engineering and Grid Computing. In the later part of thesis we have focused on problem of ontology mapping for agents on semantic web. We have precisely defined the problem and categorized it on the basis of syntactic and semantic aspects. Finally we have produced a survey of the current state of the art in ontology mapping research. In the survey we have presented some of the selected ontology mapping systems and described their functionality on the basis of the way they approach the problem, their efficiency, effectiveness and the part of problem space they cover. We consider that the survey of current state of the art in ontology mapping will provide a solid basis for further research in this field. / Ever increasing complexity of software systems, and distributed and dynamic nature of today’s enterprise level computing have initiated the demand for more self aware, flexible and robust systems, where human beings could delegate much of their work to software agents. The Semantic Web presents new opportunities for enabling, modeling, sharing and reasoning with knowledge available on the web. These opportunities are made possible through the formal representation of knowledge domains with ontologies. Semantic Web is a vision of World Wide Web (WWW) level knowledge representation system where each piece of information is equipped with well defined meaning; enabling software agents to understand and process that information. This, in turn, enables people and software agents to work in a more smooth and collaborative way. In this thesis we have first presented a detailed overview of Semantic web vision by describing its fundamental building blocks which constitutes famous layered architecture of Semantic Web. We have discussed the mile stones Semantic Web vision has achieved so far in the areas of research, education and industry and on the other hand we have presented some of the social, business and technological barriers in the way of this vision to become reality. We have also evaluated that how Semantic vision is effecting some of the current technological and research areas like Web Services, Software Agents, Knowledge Engineering and Grid Computing. In the later part of thesis we have focused on problem of ontology mapping for agents on semantic web. We have precisely defined the problem and categorized it on the basis of syntactic and semantic aspects. Finally we have produced a survey of the current state of the art in ontology mapping research. In the survey we have presented some of the selected ontology mapping systems and described their functionality on the basis of the way they approach the problem, their efficiency, effectiveness and the part of problem space they cover. We consider that the survey of current state of the art in ontology mapping will provide a solid basis for further research in this field. / Folkparksvagen 18:01,372 40 Ronneby. Sweden

Using ontologies is a key technology in the semantic web. The semantic web helps people to store their data on the web, build vocabularies, and has written rules for handling these data and also helps the search engines to distinguish between the information they want to access in web easier. In order to use multiple ontologies created by different experts we need matchers to find the similar concepts in them to use it to merge these ontologies. Text based searches use the string similarity functions to find the equivalent concepts inside ontologies using their names.This is the method that is used in lexical matchers. But a global standard for naming the concepts in different research area does not exist or has not been used. The same name may refer to different concepts while different names may describe the same concept. To solve this problem we can use another approach for calculating the similarity value between concepts which is used in structural and constraint-based matchers. It uses relations between concepts, synonyms and other information that are stored in the ontologies. Another category for matchers is instance-based that uses additional information like documents related to the concepts of ontologies, the corpus, to calculate the similarity value for the concepts. Decision trees in the area of data mining are used for different kind of classification for different purposes. Using decision trees in an instance-based matcher is the main concept of this thesis. The results of this implemented matcher using the C4.5 algorithm are discussed. The matcher is also compared to other matchers. It also is used for combination with other matchers to get a better result.

The specification of communication standards and increased availability of sensors for mobile phones and mobile systems are responsible for a significantly increasing sensor availability in populated environments. These devices are able to measure physical parameters and make this data available via communication in sensor networks. To take advantage of the so called acquiring information for public services, other parties have to be able to receive and interpret it. Locally measured datacould be seen as a means of describing user context. For a generic processing of arbitrary context data, a model for the specification ofenvironments, users, information sources and information semantics has to be defined. Such a model would, in the optimal case, enable global domain crossing context usage and hence a broader foundation for context interpretation and integration.This thesis proposes the CII-(Context Information Integration) model for the persistence and retrieval of context information in mobile, dynamically changing, environments. It discusses the terms context and context modeling under the analysis of former publications in thefield. Further-more an architecture and prototype are presented.Live and historical data are stored and accessed by the same platform and querying processor, but are treated in an optimized fashion.Optimized retrieval for closeness in n-dimensional context-spaces is supported by a dedicated method. The implementation enables self-aware,shareable agents that are able to reason or act based upon the global context,including their own. These agents can be considered as being a part of the wholecontext, being movable and executable for all context-aware applications.By applying open source technology, a gratifying implementation of CII is feasible. The document contains a thorough discussion concerning the software design and further prototype development. The use cases at the end of the document show the flexibility and extendability of the model and its implementation as a context-base for three entirely different applications. / MediaSense

Enterprise models are usually developed with ambition to capture the current or desired situation in enterprises with respect to performed or planned processes, organizational structure (including organization units, roles and competences), products or services produced and IT systems available in the enterprise.The above aspects are mutually reflective. Such enterprise models are often represented in formal modeling languages, like UEML (Unified Enterprise Modeling Language) or GEM (General Entity Manipulator) language allowing for the development of applications, which interprets or compute them. Enterprise ontologies basically allow the representation of the same aspects of an enterprise (processes, organizational structure, products and systems). However, enterprise ontologies use another representation (like OWL- Web Ontology Language) and often are developed for other application purposes than enterprise model. The objective of this thesis is to develop strategies for transforming enterprise models into enterprise ontologies.  There should be maximum preservation of semantics and minimum loss of information during the process of transformation. On the basis of meta-model (model to model) transformation, we propose three elements mapping approaches. Each approach has a number of elements mapping rules. After comparative study the best suitable approach according to objective of this thesis is selected for implementation purpose. From a technical perspective, a tool named “EM2EO” is developed, which accepts an enterprise model as input and produces ontology as output.

Business through e-commerce has become popular recently due to the massive amount of information available on internet. This has resulted in the abnormal number of reviews on websites like www.amazon.com  and www.ebay.com, where customers express their opinions about the purchases they have made. Analyzing customer’s behavior has become very important for the organizations to find new market trends and insights. For the potential customer  it becomes really difficult to get the knowledge about a product in the presence of such huge number of reviews and to sort the useful reviews and make good decision. The reviews available on these websites are in heterogeneous form i.e. structured  and unstructured form and needs to be stored in a consistent format. Since good decision requires quality information in limited amount of time, Yaakub et, al.(2011) have  proposed an ontology that uses a  multidimensional model to integrate customer’s characteristics and their comments about products. This approach first identifies the entities and then sentiments present in the customers reviews related to mobiles are transformed into an attribute table by using a 7 point polarity system (-3 to 3). The research proposed by Yaakub et, al.(2011) is in developing stage. The limitation of their approach is that the ontology proposed by them is too general. The authors have shown their desire that it should be tested for a large group of products. Also, Yaakub et, al.(2011) have used very short and simple comments for the manual extraction of features for which a sentiment has been expressed. Usually comments present on e-commerce websites are not that short and simple. In order to fulfill the aim of this thesis project, a case study has been conducted on websites www.amazon.com and www.ebay.com and the ontology proposed by  Yaakub et, al.(2011) has been refined for the three categories of mobile phones: smart phones, wet and dirty mobile phones and simple mobile phones. Further, sentiment analysis has been conducted by first using the ontology proposed by Yaakub et, al.(2011) and then by using the refined version of the ontologies for the three categories of mobile  in order to compare the results.

Today, scientists in various biomedical fields rely on biological data sources in their research. Large amounts of information concerning, for instance, genes, proteins and diseases are publicly available on the internet, and are used daily for acquiring knowledge. Typically, biological data is spread across multiple sources, which has led to heterogeneity and redundancy. The current thesis suggests grouping as one way of computationally managing biological data. A conceptual model for this purpose is presented, which takes properties specific for biological data into account. The model defines sub-tasks and key issues where multiple solutions are possible, and describes what approaches for these that have been used in earlier work. Further, an implementation of this model is described, as well as test cases which show that the model is indeed useful. Since the use of ontologies is relatively new in the management of biological data, the main focus of the thesis is on how semantic similarity of ontological annotations can be used for grouping. The results of the test cases show for example that the implementation of the model, using Gene Ontology, is capable of producing groups of data entries with similar molecular functions.

Module extraction is the task of computing a (preferably small) fragment <i>M</i> of an ontology <i>O</i> that preserves a class of entailments over a signature of interest ∑. Existing practical approaches ensure that <i>M</i> preserves all second-order entailments of <i>O</i> over ∑, which is a stronger condition than is required in many applications. In the first part of this thesis, we propose a novel approach to module extraction which, based on a reduction to a datalog reasoning problem, makes it possible to compute modules that are tailored to preserve only specific kinds of entailments. This leads to obtaining modules that are often significantly smaller than those produced by other practical approaches, as shown in an empirical evaluation. In the second part of this thesis, we consider the application of module extraction to the optimisation of ontology classification. Classification is a fundamental reasoning task in ontology design, and there is currently a wide range of reasoners that provide this service. Reasoners aimed at so-called lightweight ontology languages are much more efficient than those aimed at more expressive ones, but they do not offer completeness guarantees for ontologies containing axioms outside the relevant language. We propose an original approach to classification based on exploiting module extraction techniques to divide the workload between a general purpose reasoner and a more efficient reasoner for a lightweight language in such a way that the bulk of the workload is assigned to the latter. We show how the proposed approach can be realised using two particular module extraction techniques, including the one presented in the first part of the thesis. Furthermore, we present the results of an empirical evaluation that shows that this approach can lead to a significant performance improvement in many cases.

La gestion des connaissances dans les petites et moyennes entreprises a toujours été un défi. Ces entreprises ont des caractéristiques particulières qui sont liés à la taille, la structure et la coordination et la collaboration de leurs membres. L’enjeu scientifique de ce travail est de mieux appréhender les spécificités de la gestion des connaissances et de l’apprentissage organisationnel dans ces petites entreprises. / Knowledge management in small and medium enterprises has always been a challenge. These companies have special features that are related to the size, structure and coordination and cooperation between members. The scientific challenge of this work is to better understand the specifics of knowledge management and organizational leaning in these small businesses.