L'aide à la décision se fonde sur l'observation d'un environnement évolutif dont on scrute les évènements. Ces évènements peuvent être de différentes natures, dont les connexions qui peuvent se créer au sein d'un réseau d'acteurs. L'observation des bases documentaires ne semble plus suffisante pour nourrir l'aide à la décision. En effet, les nouveaux outils de communication et de collaboration, dont l'usage se répand rapidement au sein des organisations, sont sources de nouvelles formes d'informations peu ou mal utilisées par les systèmes actuels d'aide à la décision des organisations. L'objectif de la thèse est de concevoir une plate-forme (modélisation et développement) pour les organisations permettant à leurs membres de bénéficier de médias sociaux et à leurs décideurs de bénéficier d'outils d'aide à la décision prenant en compte tous les types de ressources circulant sur cette plate-forme. / Decision support is partly based on the observation of a dynamic and mutating environment (Situation Awareness). The events of such environments can be of different types, including new relations created within a network of actors. We think classical documentary databases are no longer sufficient to serve situation awarness. The quick spread and adoption of new communication and collaboration tools in organizations, bring new kind of information, like the social network of the organization, which are currently not or badly taken in account by organizational decision support systems. The aim of this thesis is to design a platform, which provides to organizations both the social media to help their members to collaborate and the decision tools, which take into account all types of information exchanged in the platform.

Dans un environnement collaboratif de développement de produit, plusieurs acteurs, ayant différents points de vue et intervenant dans plusieurs phases du cycle de vie de produit, doivent communiquer et échanger des connaissances entre eux. Ces connaissances, existant sous différents formats hétérogènes, incluent potentiellement plusieurs concepts tels que l’historique de conception, la structure du produit, les features, les paramètres, les contraintes, et d’autres informations sur le produit. Les exigences industrielles de réduction du temps et du coût de production nécessitent l’amélioration de l’interopérabilité sémantique entre les différents processus de développement afin de surmonter ces problèmes d’hétérogénéité tant au niveau syntaxique, structurel, que sémantique. Dans le domaine de la CAO, la plupart des méthodes existantes pour l’échange de données d’un modèle de produit sont, effectivement, basées sur le transfert des données géométriques. Cependant, ces données ne sont pas suffisantes pour saisir la sémantique des données, telle que l’intention de conception, ainsi que l’édition des modèles après leur échange. De ce fait, nous nous sommes intéressés à l’échange des modèles « intelligents », autrement dit, définis en termes d’historique de construction, de fonctions intelligentes de conception appelées features, y compris les paramètres et les contraintes. L’objectif de notre thèse est de concevoir des méthodes permettant d’améliorer l’interopérabilité sémantique des systèmes CAO moyennant les technologies du Web Sémantique comme les ontologies OWL DL et le langage des règles SWRL. Nous avons donc élaboré une approche d’échange basée sur une ontologie commune de features de conception, que nous avons appelée CDFO « Common Design Features Ontology », servant d’intermédiaire entre les différents systèmes CAO. Cette approche s’appuie principalement sur deux grandes étapes. La première étape consiste en une homogénéisation des formats de représentation des modèles CAO vers un format pivot, en l’occurrence OWL DL. Cette homogénéisation sert à traiter les hétérogénéités syntaxiques entre les formats des modèles. La deuxième étape consiste à définir des règles permettant la mise en correspondance sémantique entre les ontologies d’application de CAO et notre ontologie commune. Cette méthode de mise en correspondance se base principalement, d’une part, sur la définition explicite des axiomes et des règles de correspondance permettant l’alignement des entités de différentes ontologies, et d’autre part sur la reconnaissance automatique des correspondances sémantiques supplémentaires à l’aide des capacités de raisonnement fournies par les moteurs d’inférence basés sur les logiques de description. Enfin, notre méthode de mise en correspondance est enrichie par le développement d’une méthode de calcul de similarité sémantique appropriée pour le langage OWL DL, qui repose principalement sur les composants des entités en question tels que leur description et leur contexte. / A major issue in product development is the exchange and sharing of product knowledge among many actors. This knowledge includes many concepts such as design history, component structure, features, parameters, constraints, and more. Heterogeneous tools and multiple designers are frequently involved in collaborative product development, and designers often use their own terms and definitions to represent a product design. Thus, to efficiently share design information among multiple designers, the design intent should be persistently captured and the semantics of the modeling terms should be semantically processed both by design collaborators and intelligent systems. Regarding CAD models, most of the current CAD systems provide feature-based design for the construction of solid models. Features are devised to carry, semantically, product information throughout its life cycle. Consequently, features should be maintained in a CAD model during its migration among different applications. However, existing solutions for exchanging product information are limited to the process of geometrical data, where semantics assigned to product model are completely lost during the translation process. Current standards, such as ISO 10303, known as STEP have attempted to solve this problem, but they define only syntactic data representation so that semantic data integration is not possible. Moreover, STEP does not provide a sound basis to reason with knowledge. Our research investigates the use of Semantic Web technologies, such as ontologies and rule languages; e.g. SWRL, for the exchange of “intelligent” CAD models among different systems, while maintaining the original relations among entities of the model. Thus, we have proposed an ontological approach based on the construction of a common design features ontology, used as an Interlingua for the exchange of product data. This ontology is represented formally with OWL DL. Furthermore, axioms and mapping rules are defined to achieve the semantic integration between the applications ontologies and the common ontology. The integration process relies basically on reasoning capabilities provided by description logics in order to recognize automatically additional mappings among ontologies entities. Furthermore, the mapping process is enhanced with a semantic similarity measure in order to detect similar design features. However, this will enable data analysis, as well as manage and discover implicit relationships among product data based on semantic modeling and reasoning.

Cette thèse porte sur la transformation automatique et sémantique de règles métiers en des règles formelles. Ces règles métiers sont originellement rédigées sous la forme de textes en langage naturel, de tableaux et d'images. L'objectif est de mettre à la disposition des experts métiers, un ensemble de services leur permettant d'élaborer des corpus de règles métiers formelles. Le domaine de la Construction est le champ d'application de ces travaux. Disposer d'une version formelle et exécutable de ces règles métiers servira à effectuer des contrôles de conformité automatique sur les maquettes numériques des projets de construction en cours de conception.Pour cela, nous avons mis à disposition des experts métiers les deux principales contributions de cette thèse. La première est la mise sur pied d'un langage naturel contrôlé, dénommé RAINS. Il permet aux experts métiers de réécrire les règles métiers sous la forme de règles formelles. Les règles RAINS se composent de termes du vocabulaire métier et de mots réservés tels que les fonctions de comparaisons, les marques de négation et de quantification universelle et les littéraux. Chaque règle RAINS a une sémantique formelle unique qui s'appuie sur les standards du web sémantique. La seconde contribution majeure est un service de formalisation des règles métiers. Ce service implémente une approche de formalisation proposée dans le cadre de cette thèse et dénommée FORSA. Ce service propose des versions RAINS des règles métiers en langage naturel qui lui sont soumises. FORSA fait appel à des outils du traitement automatique du langage naturel et à des heuristiques. Pour évaluer FORSA, nous avons mis sur pied un benchmark adapté à la tâche de formalisation des règles métiers. Les données de ce benchmark sont issues de normes du domaine de la Construction / This thesis focuses on automatic and semantic transformation of business rules into formal rules. These business rules are originally drafted in the form of natural language text, tables and images. Our goal is to provide to business experts a set of services allowing them to develop corpora of formal business rules. We carry out this work in the field of building engineering construction. Having formal and executable versions of the business rules enables to perform automatic compliance checking of digital mock-ups of construction projects under design.For this we made available to business experts, the two main contributions of this thesis. The first is the development of a controlled natural language, called RAINS. It allows business experts to rewrite business rules in the form of formal rules. A RAINS rule consists of terms of the business vocabulary and reserved words such as comparison predicates, negation and universal quantification markers and literals. Each RAINS rule has a unique formal semantics which is based on the standards of the Semantic Web. The second major contribution is a service for formalization of business rules. This service implements a formalized approach proposed in this thesis and called FORSA. This service offers RAINS versions of natural language business rules submitted to it. FORSA uses natural language processing tools and heuristics. To evaluate FORSA, we have set up a benchmark adapted to the formalization of business rules task. The dataset from this benchmark are from norms in the field of Construction

A Web Semântica é uma maneira de explorar a associação de significados explícitos aos conteúdos de documentos presentes na Web, para que esses possam ser processados diretamente ou indiretamente por máquinas. Para possibilitar esse processamento, os computadores necessitam ter acesso a coleções estruturadas de informações e a conjuntos de regras de inferência sobre esses conteúdos. O SWRL permite a combinação de regras e termos de ontologias (definidos por OWL) para aumentar a expressividade de ambos. Entretanto, conforme um conjunto de regras cresce, ele torna-se de difícil compreensão e sujeito a erros, especialmente quando mantido por mais de uma pessoa. Para que o SWRL se torne um verdadeiro padrão web, deverá ter a capacidade de lidar com grandes conjuntos de regras. Para encontrar soluções para este problema, primeiramente, foi realizado um levantamento sobre sistemas de regras de negócios, descobrindo os principais recursos e interfaces utilizados por eles, e então, com as descobertas, propusemos técnicas que usam novas representações visuais em uma aplicação web. Elas permitem detecção de erro, identificação de regras similares, agrupamento, visualização de regras e o reuso de átomos para novas regras. Estas técnicas estão implementadas no SWRL Editor, um plug-in open-source para o Web-Protégé (um editor de ontologias baseado na web) que utiliza ferramentas de colaboração para permitir que grupos de usuários possam não só ver e editar regras, mas também comentar e discutir sobre elas. Foram realizadas duas avaliações do SWRL Editor. A primeira avaliação foi um estudo de caso para duas ontologias da área biomédica (uma área onde regras SWRL são muito usadas) e a segunda uma comparação com os únicos três editores de regras SWRL encontrados na literatura. Nessa comparação foi mostrando que ele implementa mais recursos encontrados em sistemas de regras em geral / The Semantic Web is a way to associate explicitly meaning to the content of web documents to allow them to be processed directly by machines. To allow this processing, computers need to have access to structured collections of information and sets of rules to reason about these content. The Semantic Web Rule Language (SWRL) allows the combination of rules and ontology terms, defined using the Web Ontology Language (OWL), to increase the expressiveness of both. However, as rule sets grow, they become difficult to understand and error prone, especially when used and maintained by more than one person. If SWRL is to become a true web standard, it has to be able to handle big rule sets. To find answers to this problem, we first surveyed business rule systems and found the key features and interfaces they used and then, based on our finds, we proposed techniques and tools that use new visual representations to edit rules in a web application. They allow error detection, rule similarity analysis, rule clustering visualization and atom reuse between rules. These tools are implemented in the SWRL Editor, an open source plug-in for Web-Protégé (a web-based ontology editor) that leverages Web-Protégés collaborative tools to allow groups of users to not only view and edit rules but also comment and discuss about them. We have done two evaluations of the SWRL Editor. The first one was a case study of two ontologies from the biomedical domain, the second was a comparison with the SWRL editors available in the literature, there are only three. In this comparison, it has been shown that the SWRL Editor implements more of the key resources found on general rule systems than the other three editors

A Plataforma Lattes é uma excelente base de dados de pesquisadores para a sociedade brasileira, adotada pela maioria das instituições de fomento, universidades e institutos de pesquisa do País. Entretanto, é limitada quanto à exibição de dados sumarizados de um grupos de pessoas, como por exemplo um departamento de pesquisa ou os orientandos de um ou mais professores. Diversos projetos já foram desenvolvidos propondo soluções para este problema, alguns inclusive desenvolvendo ontologias a partir do domínio de pesquisa. Este trabalho tem por objetivo integrar todas as funcionalidades destas ferramentas em uma única solução, a SOS Lattes. Serão apresentados os resultados obtidos no desenvolvimento desta solução e como o uso de ontologias auxilia nas atividades de identificação de inconsistências de dados, consultas para construção de relatórios consolidados e regras de inferência para correlacionar múltiplas bases de dados. Além disto, procura-se por meio deste trabalho contribuir com a expansão e disseminação da área de Web Semântica, por meio da criação de uma ferramenta capaz de extrair dados de páginas Web e disponibilizar sua estrutura semântica. Os conhecimentos adquiridos durante a pesquisa poderão ser úteis ao desenvolvimento de novas ferramentas atuando em diferentes ambientes. / The Lattes Platform is an excellent database of researchers for the Brazilian society , adopted by most Brazilian funding agencies, universities and research institutes. However, it is limited as to displaying summarized data from a group of people, such as a research department or students supervised by one or more professor. Several projects have already been developed which propose solutions to this problem, including some developing ontologies from the research domain. This work aims to integrate all the functionality of these tools in a single solution, SOS Lattes. The results obtained in the development of this solution are presented as well as the use of ontologies to help identifying inconsistencies in the data, queries for building consolidated reports and rules of inference for correlating multiple databases. Also, this work intends to contribute to the expansion and dissemination of the Semantic Web, by creating a tool that can extract data from Web pages and provide their semantic structure. The knowledge gained during the study may be useful for the development of new tools operating in different environments.

[pt] Inicialmente a Internet era um canal de comunicação e distribuição de conteúdo textual. Com o advento do comércio eletrônico e a maturação da própria Web, ela se tornou uma plataforma de implantação de sistemas. Paralelamente, o próprio desenvolvimento de software evoluiu, com aplicações de novos conceitos da engenharia de software, tais como componentes, reuso e design patterns. No intuito de desenvolver práticas e tecnologias que aperfeiçoem a construção de softwares decidiu-se, neste trabalho, por conciliar estas duas tendências evolutivas. Swell é um ambiente para o auxílio no desenvolvimento de Sistemas Baseados na Web (SBWs), tendo como ponto forte a seleção semântica de web services. Trata-se de uma ferramenta cujo objetivo é ajudar nas tarefas de design e construção de aplicações, visando atingir alto grau de reuso. Este auxílio se dá através da descrição, busca e escolha de serviços para composição de aplicações. O ambiente Swell foi pensado de modo a dar suporte à evolução das tecnologias de componentes para Web. Ele provê pontos de flexibilização permitindo adaptação a novas descrições de web services e refletindo estas mudanças no mecanismo de busca. / [en] Initially the Internet was a communication channel and a text-based content dissemination vehicle. With the advent of e-commerce and Web's unfolding, it became a systems deployment platform. At the same time, software development also evolved, through the use of new software engineering concepts, such as components, reuse and design patterns. This work aims at helping to develop practices and technologies that improve software development by conciliating these two evolution trends. Swell is an environment that helps Web Based Systems development, having as a cornerstone the semantic selection of web- services. It is a tool whose objective is to help in the application design and development tasks, aiming to reach a high degree of reuse. This is achieved through the support for the description, search and selection of web services for application composition. The Swell environment was conceived as a framework with support for the evolution of web components technologies. It provides hot spots that allow for adaptation of new web services descriptions and reflection of these changes in the search engine.

[pt] A Web Semântica já está deixando de ser uma visão de Tim Berners-Lee para virar uma realidade. Diversos projetos em todo mundo já exploram as potencialidades dessa segunda geração da Web para tornar seu conteúdo processável por máquinas. Infelizmente (ou felizmente!), os computadores não são capazes de desenvolver por livre e espontânea vontade uma linguagem consensual de comunicação. É nesta etapa que entram em cena as ontologias. Conhecida no ramo da filosofia como teorias sobre a natureza da existência, as ontologias têm sido encaradas de maneira consideravelmente diferente no universo computacional. Representam um entendimento comum e compartilhado sobre um domínio específico e têm como o objetivo principal permitir a comunicação entre organizações, pessoas e o uso de metodologias, processos e/ ou métodos bem definidos. Com o aumento de sua importância, o projeto e desenvolvimento de ontologias vêm deixando de ser uma arte para se transformar em um processo de engenharia. Diversas propostas já foram apresentadas para o problema de engenharia de ontologias, muitas delas apoiadas por estudos acadêmicos e industriais. No entanto, é importante notar que nenhuma dessas metodologias - que em muitos casos resumem-se apenas a heurísticas extraídas da experiência de seus autores ou a orientações sobre como alcançar bons resultados - atende por completo aos requisitos potenciais do projeto de uma ontologia. O que se propõe neste trabalho é a unificação de disciplinas e atividades oriundas de metodologias distintas em um framework de processos abrangente, entitulado KUP - Knowldge Unified Process, que permite um adequado projeto e desenvolvimento de ontologias e bases de conhecimento. Entende-se como um processo um conjunto de atividades e resultados associados a essas atividades com o objetivo de garantir a geração de um produto final, seja este produto um software, uma ontologia ou uma ontologia associada a sua base de conhecimento. A unificação deste processo segue as melhores práticas em engenharia de software existentes na indústria e é apoiada por um framework de avaliação de metodologias consolidado na academia. Foram realizados dois estudos de caso para este framework de processos, um envolvendo o desenvolvimento de uma solução de gestão de conhecimento em segurança da informação e outro envolvendo a integração de uma ferramenta de gestão de competências para ambientes de educação a distância. / [en] The Semantic Web is now a reality. Several projects all around the world are already using tools technologies developed to support the second generation of the Web to provide machine-processable content for software agents, web services and applications. However, computers can not agree on a consesual language by themselves. Ontologies can be used as a way to provide this shared conceptualization, making posssible the desired communication among organizations, people and apllications. Several proposals have been already presented regarding ontology engineering - many supported by academic and industrial case studies. However, none of them encompasses all the requirements identified for an ontology construction project. This work describes the unification of different features extracted from those methodologies to build a process framework named KUP - the Knowledge Unified Process. This unified process is based on several industry best practices and on a well accepted ontology methodology evaluation framework. Two case studies were developed so as to support and validate this process framework. The first was the development of a semantic web solution for security information knowledge management and the second one was the integration of a skill management tool to a learning management system, through ontologies.

Les sciences de la vie produisent de grandes masses de données (par exemple, des essais cliniques et des articles scientifiques). L'intégration et l'analyse des différentes bases de données liées à la même question de recherche, par exemple la corrélation entre phénotypes et génotypes, sont essentielles pour découvrir de nouvelles connaissances. Pour cela, la communauté des sciences de la vie a adopté les techniques du Web sémantique pour réaliser l'intégration et l'interopérabilité des données, en particulier les ontologies. En effet, les ontologies représentent la brique de base pour représenter et partager la quantité croissante de données sur le Web. Elles fournissent un vocabulaire commun pour les humains, et des définitions d'entités formelles pour les machines.Un grand nombre d'ontologies et de terminologies biomédicales a été développé pour représenter et annoter les différentes bases de données existantes. Cependant, celles qui sont représentées avec différentes ontologies qui se chevauchent, c'est à dire qui ont des parties communes, ne sont pas interopérables. Il est donc crucial d'établir des correspondances entre les différentes ontologies utilisées, ce qui est un domaine de recherche actif connu sous le nom d'alignement d'ontologies.Les premières méthodes d'alignement d'ontologies exploitaient principalement le contenu lexical et structurel des ontologies à aligner. Ces méthodes sont moins efficaces lorsque les ontologies à aligner sont fortement hétérogènes lexicalement, c'est à dire lorsque des concepts équivalents sont décrits avec des labels différents. Pour pallier à ce problème, la communauté d'alignement d'ontologies s'est tournée vers l'utilisation de ressources de connaissance externes en tant que pont sémantique entre les ontologies à aligner. Cette approche soulève plusieurs nouvelles questions de recherche, notamment : (1) la sélection des ressources de connaissance à utiliser, (2) l'exploitation des ressources sélectionnées pour améliorer le résultat d'alignement. Plusieurs travaux de recherche ont traité ces problèmes conjointement ou séparément. Dans notre thèse, nous avons fait une revue systématique et une comparaison des méthodes proposées dans la littérature. Puis, nous nous sommes intéressés aux deux questions.Les ontologies, autres que celles à aligner, sont les ressources de connaissance externes (Background Knowledge : BK) les plus utilisées. Les travaux apparentés sélectionnent souvent un ensemble d'ontologies complètes en tant que BK même si, seuls des fragments des ontologies sélectionnées sont réellement efficaces pour découvrir de nouvelles correspondances. Nous proposons une nouvelle approche qui sélectionne et construit une ressource de connaissance à partir d'un ensemble d'ontologies. La ressource construite, d'une taille réduite, améliore, comme nous le démontrons, l'efficience et l'efficacité du processus d'alignement basé sur l'exploitation de BK.L'exploitation de BK dans l'alignement d'ontologies est une épée à double tranchant : bien qu'elle puisse augmenter le rappel (i.e., aider à trouver plus de correspondances correctes), elle peut réduire la précision (i.e., générer plus de correspondances incorrectes). Afin de faire face à ce problème, nous proposons deux méthodes pour sélectionner les correspondances les plus pertinentes parmi les candidates qui se basent sur : (1) un ensemble de règles et (2) l'apprentissage automatique supervisé. Nous avons expérimenté et évalué notre approche dans le domaine biomédical, grâce à la profusion de ressources de connaissances en biomédecine (ontologies, terminologies et alignements existants). Nous avons effectué des expériences intensives sur deux benchmarks de référence de la campagne d'évaluation de l'alignement d'ontologie (OAEI). Nos résultats confirment l'efficacité et l'efficience de notre approche et dépassent ou rivalisent avec les meilleurs résultats obtenus. / Life sciences produce a huge amount of data (e.g., clinical trials, scientific articles) so that integrating and analyzing all the datasets related to a given research question like the correlation between phenotypes and genotypes, is a key element for knowledge discovery. The life sciences community adopted Semantic Web technologies to achieve data integration and interoperability, especially ontologies which are the key technology to represent and share the increasing amount of data on the Web. Indeed, ontologies provide a common domain vocabulary for humans, and formal entity definitions for machines.A large number of biomedical ontologies and terminologies has been developed to represent and annotate various datasets. However, datasets represented with different overlapping ontologies are not interoperable. It is therefore crucial to establish correspondences between the ontologies used; an active area of research known as ontology matching.Original ontology matching methods usually exploit the lexical and structural content of the ontologies to align. These methods are less effective when the ontologies to align are lexically heterogeneous i.e., when equivalent concepts are described with different labels. To overcome this issue, the ontology matching community has turned to the use of external knowledge resources as a semantic bridge between the ontologies to align. This approach arises several new issues mainly: (1) the selection of these background resources, (2) the exploitation of the selected resources to enhance the matching results. Several works have dealt with these issues jointly or separately. In our thesis, we made a systematic review and historical evaluation comparison of state-of-the-art approaches.Ontologies, others than the ones to align, are the most used background knowledge resources. Related works often select a set of complete ontologies as background knowledge, even if, only fragments of the selected ontologies are actually effective for discovering new mappings. We propose a novel BK-based ontology matching approach that selects and builds a knowledge resource with just the right concepts chosen from a set of ontologies. The conducted experiments showed that our BK selection approach improves efficiency without loss of effectiveness.Exploiting background knowledge resources in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). We propose two methods to select the most relevant mappings from the candidate ones: (1) based on a set of rules and (2) with Supervised Machine Learning. We experiment and evaluate our approach in the biomedical domain, thanks to the profusion of knowledge resources in biomedicine (ontologies, terminologies and existing alignments).We evaluated our approach with extensive experiments on two Ontology Alignment Evaluation Initiative (OAEI) benchmarks. Our results confirm the effectiveness and efficiency of our approach and overcome or compete with state-of-the-art matchers exploiting background knowledge resources.

A Plataforma Lattes é, hoje, a principal base de currículos dos pesquisadores brasileiros. Os currículos da Plataforma Lattes armazenam de forma padronizada dados profissionais, acadêmicos, de produções bibliográficas e outras informações dos pesquisadores. Através de uma base de Currículos Lattes, podem ser gerados vários tipos de relatórios consolidados. As ferramentas existentes da Plataforma Lattes não são capazes de detectar alguns problemas que aparecem na geração dos relatórios consolidados como duplicidades de citações ou produções bibliográficas classificadas de maneiras distintas por cada autor, gerando um número total de publicações errado. Esse problema faz com que os relatórios gerados necessitem ser revistos pelos pesquisadores e essas falhas deste processo são a principal inspiração deste projeto. Neste trabalho, utilizamos como fonte de informações currículos da Plataforma Lattes para popular uma ontologia e utilizá-la principalmente como uma base de dados a ser consultada para geração de relatórios. Analisamos todo o processo de extração de informações a partir de arquivos HTML e seu posterior processamento para inserí-las corretamente dentro da ontologia, de acordo com sua semântica. Com a ontologia corretamente populada, mostramos também algumas consultas que podem ser realizadas e fazemos uma análise dos métodos e abordagens utilizadas em todo processo, comentando seus pontos fracos e fortes, visando detalhar todas as dificuldades existentes no processo de população (instanciação) automática de uma ontologia. / Lattes Platform is the main database of Brazilian researchers resumés in use nowadays. It stores in a standardized form professional, academic, bibliographical productions and other data from these researchers. From these Lattes resumés database, several types of reports can be generated. The tools available for Lattes platform are unable to detect some of the problems that emerge when generating consolidated reports, such as citation duplicity or bibliographical productions misclassified by their authors, generating an incorrect number of publications. This problem demands a revision performed by the researcher on the reports generated, and the flaws of this process are the main inspiration for this project. In this work we use the Lattes platform resumés database as the source for populating an ontology that is intended to be used to generate reports. We analyze the whole process of information gathering from HTML files and its post-processing to insert them correctly in the ontology, according to its semantics. With this ontology correctly populated, we show some new reports that can be generated and we perform also an analysis of the methods and approaches used in the whole process, highlighting their strengths and weaknesses, detailing the dificulties faced in the automated populating process (instantiation) of an ontology.

Made available in DSpace on 2016-03-22T17:26:25Z (GMT). No. of bitstreams: 1 Dissertaandreo II Final.pdf: 5215323 bytes, checksum: 78dc4aa3677577cccbd14cb124510b6a (MD5) Previous issue date: 2010-03-26 / Until recently, the handling of large amounts of information was a task for specialists, is now a need for people from all areas, in their professional and in the tasks of everyday life. The quality in information retrieval is crucial in many professions, and improvement of systems can have a major impact, especially when it comes to heterogeneous collections of documents. The research on the Web has been an excellent field for information retrieval and massive automatic indexing on a large scale. This research proposes a general study on Semantic Web, Web 2.0, Social Networks and Ontologies, its applications, languages and methodologies, with the main purpose of creating of an Social Networks Ontology, in order to see how the social networks can be enhanced through the Semantic Web, and see how in the future the Semantic Web and Social Networks will be integrated. The big challenge is to mold this field in a manner to facilitate the development of applications for it, using technologies and tools of the Semantic Web. This work was also intended to systematize the study of Social Networks, Web 2.0, Semantic Web and Ontologies in order to serve as a basis and reference for future studies / Até recentemente, a manipulação de grandes quantidades de informação era uma tarefa de especialistas, agora constitui uma necessidade para pessoas de todas as áreas, tanto na sua atividade profissional como na maioria das tarefas do dia a dia. A qualidade na recuperação de informação é crucial em muitas profissões, e a melhoria dos sistemas pode ter um grande impacto, especialmente quando se trata de coleções de documentos heterogêneos. A pesquisa na Web tem sido um excelente campo para a recuperação de informação em grande escala e a indexação automática maciça. O presente trabalho propõe um estudo geral sobre Web Semântica, Web 2.0, Redes Sociais e Ontologias, suas aplicações, linguagens e metodologias, tendo como finalidade principal, a criação de uma Ontologia do Domínio das Redes Sociais, com os objetivos de verificar como as Redes Sociais podem ser potencializadas através da Web Semântica, e de como no futuro a Web Semântica e as Redes Sociais irão se integrar. O grande desafio consiste em modelar este domínio de uma forma a facilitar o desenvolvimento de aplicações para ele, utilizando tecnologias e ferramentas da Web Semântica. Este trabalho teve também como objetivo sistematizar o estudo das Redes Sociais, Web 2.0, Web Semântica e Ontologias, de forma a servir de base e referência para estudos futuros