Made available in DSpace on 2016-06-02T19:35:48Z (GMT). No. of bitstreams: 1 4959.pdf: 10308984 bytes, checksum: e2033d7b11f298cd6cd6904dc84e6c3b (MD5) Previous issue date: 2013-02-22 / Universidade Federal de Sao Carlos / This thesis entitled "Professionalism in Teacher Rural Education analyzes the process of formation of the teaching profession of educators in the field of Breves riversidemultigrade classes on the island of Marajó, state of Pará, through the subject matter of teaching based on its implications professional educators in in rural areas in multigrade classesriverside. This involved the following research question: For what reasons and educational practices that constitute the teaching profession of educators from the field in multigrade classes riverside? This, from the perspective of the discussion of knowledge base, while professional competence of professionalism that transcends the domain of skills and techniques and emerges from the interaction between moral obligation and commitment to the community where the school is; professional development of teaching from the standpoint of studies of Rural Education. This study was conducted under the framework of investigative historical and dialectical materialism and the contour ethnographic research in order to characterize the work in the multigrade teaching riparian profiles and attitudes of educators teaching intervention in the schooling process, institutional contribution to the teachers in the field and some aspects of community organization with respect to the structuring of schooling processes and dynamics of human development in rural areas. / Esta tese intitulada Profissionalidade docente na Educação do Campo objetiva analisar o processo de constituição da profissionalidade docente de educadores do campo em turmas multisseriadas ribeirinhas de Breves, na ilha de Marajó, estado do Pará. Tem seu objeto de estudo na ação educativa em escolas multisseriadas e suas implicações no desenvolvimento profissional da docência de educadores do campo. Para tanto, contou com a seguinte questão de pesquisa: Por quais fundamentos e práticas educativas se constitui a profissionalidade docente de educadores do campo em turmas multisseriadas ribeirinhas? Isto, tendo em vista que a profissionalidade enquanto competência profissional no campo, transcende o domínio de habilidades e técnicas e emerge a partir da interação entre o compromisso político, para com a competência técnica e o domínio científico, junto às comunidades em que estão sediadas as escolas em que trabalham os docentes da Educação do Campo. Este estudo se desenvolveu sob a perspectiva de análise do materialismo histórico-dialético e da pesquisa de contorno etnográfico em função da necessária análise e da caracterização dos trabalhos docentes na multissérie ribeirinha. Além de se investigar acerca dos perfis e posturas das intervenções didáticas deflagradas pelos educadores do campo em meio ao processo de escolarização no campo. O aporte institucional usufruído pelos docentes da Educação do Campo e as limitações e possibilidades de seus engajamentos na organização comunitária também foram observados e analisados neste trabalho. O que se deu em função da importância que possuem tais elementos na estruturação das práticas de escolarização e nas dinâmicas de formação humana que atravessam de modo central a constituição da profissionalidade docente na Educação do Campo.

Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory.

Internet offre une énorme quantité d’informations à portée de main et dans une telle variété de sujets, que tout le monde est en mesure d’accéder à une énorme variété de connaissances. Une telle grande quantité d’information pourrait apporter un saut en avant dans de nombreux domaines (moteurs de recherche, réponses aux questions, tâches NLP liées) si elle est bien utilisée. De cette façon, un enjeu crucial de la communauté d’intelligence artificielle a été de recueillir, d’organiser et de faire un usage intelligent de cette quantité croissante de connaissances disponibles. Heureusement, depuis un certain temps déjà des efforts importants ont été faits dans la collecte et l’organisation des connaissances, et beaucoup d’informations structurées peuvent être trouvées dans des dépôts appelés Bases des Connaissances (BCs). Freebase, Entity Graph Facebook ou Knowledge Graph de Google sont de bons exemples de BCs. Un grand problème des BCs c’est qu’ils sont loin d’êtres complets. Par exemple, dans Freebase seulement environ 30% des gens ont des informations sur leur nationalité. Cette thèse présente plusieurs méthodes pour ajouter de nouveaux liens entre les entités existantes de la BC basée sur l’apprentissage des représentations qui optimisent une fonction d’énergie définie. Ces modèles peuvent également être utilisés pour attribuer des probabilités à triples extraites du Web. On propose également une nouvelle application pour faire usage de cette information structurée pour générer des informations non structurées (spécifiquement des questions en langage naturel). On pense par rapport à ce problème comme un modèle de traduction automatique, où on n’a pas de langage correct comme entrée, mais un langage structuré. Nous adaptons le RNN codeur-décodeur à ces paramètres pour rendre possible cette traduction. / Internet provides a huge amount of information at hand in such a variety of topics, that now everyone is able to access to any kind of knowledge. Such a big quantity of information could bring a leap forward in many areas if used properly. This way, a crucial challenge of the Artificial Intelligence community has been to gather, organize and make intelligent use of this growing amount of available knowledge. Fortunately, important efforts have been made in gathering and organizing knowledge for some time now, and a lot of structured information can be found in repositories called Knowledge Bases (KBs). A main issue with KBs is that they are far from being complete. This thesis proposes several methods to add new links between the existing entities of the KB based on the learning of representations that optimize some defined energy function. We also propose a novel application to make use of this structured information to generate questions in natural language.

[pt] Nos últimos anos temos visto o crescimento do volume de dados não estruturados gerados naWeb tradicional, e por isso aWeb Semântica nasceu como um paradigma que se propõe a estruturar o conteúdo da Web de uma forma flexível, por meio de ontologias de domínio e o modelo RDF, tornando os computadores capazes de processar automaticamente esses dados e possibilitando a geração de mais informação e conhecimento. Mas para tornar estas informações acessíveis para usuários de outros domínios, é necessário que haja uma maneira mais conveniente de consultar estas bases de conhecimento. A área de Processamento de Linguagem Natural (PLN) forneceu ferramentas para permitir que a linguagem natural (falada ou escrita) seja um meio conveniente para realizar consultas em bases de conhecimento. Contudo, para que o uso da linguagem natural seja realmente efetivo, é necessário um método que converta uma pergunta ou pedido em linguagem natural em uma consulta estruturada. Tendo em vista este objetivo, o presente trabalho propõe uma abordagem que converte uma pergunta/pedido em Português em uma consulta estruturada na linguagem SPARQL, por meio do uso de árvores de dependências e ontologias estruturada em grafos, e que também permite o enriquecimento dos resultados das perguntas/pedidos por meio da geração de perguntas relacionadas. / [en] In recent years we have seen the growth of the volume of unstructured data generated in the traditional Web. Therefore the Semantic Web was born as a paradigm that proposes to structure the content of the Web flexibly through domain ontologies and the RDF model, making computers capable of automatically processing this data, enabling the generation of more information and knowledge. However, to make this information accessible to users in other domains, there needs to be a more convenient way of looking at these knowledge bases. The Natural Language Processing (NLP) area has provided tools to allow natural (spoken or writing) is a convenient way to perform queries in knowledge bases. However, for the use of natural language to be useful, a method is required that converts a natural language question or request into a structured query. With this objective, the present work proposes an approach that converts a question/request in Portuguese into a structured query in the SPARQL language, through the use of dependency trees and structured ontologies in graphs, and that also enables the enrichment of question/request results by generating related questions.

Description Logics (DLs) are a family of knowledge representation formalisms used for terminological reasoning. They have a wide range of applications such as medical knowledge-bases, or the semantic web. Research on DLs has been focused on the development of sound and complete inference algorithms to decide satisfiability and subsumption for increasingly expressive DLs. Non-standard inferences are a group of relatively new inference services which provide reasoning support for the building, maintaining, and deployment of DL knowledge-bases. So far, non-standard inferences are not available for very expressive DLs. In this paper we present first results on non-standard inferences for DLs with transitive roles. As a basis, we give a structural characterization of subsumption for DLs where existential and value restrictions can be imposed on transitive roles. We propose sound and complete algorithms to compute the least common subsumer (lcs).

In Description Logics the reasoning service most specific concept (msc) constructs a concept description that generalizes an ABox individual into a concept description. For the Description Logic EL the msc may not exist, if computed with respect to general EL-TBoxes or cyclic ABoxes. However, it is still possible to find a concept description that is the msc up to a fixed role-depth, i.e. with respect to a maximal nesting of quantifiers. In this report we present a practical approach for computing the roledepth bounded msc, based on the polynomial-time completion algorithm for EL. We extend these methods to Prob-EL⁰¹c , which is a probabilistic variant of EL. Together with the companion report [9] this report devises computation methods for the bottom-up construction of knowledge bases for EL and Prob-EL⁰¹c .

PURPOSE: To demonstrate the feasibility of using a knowledge base of prior treatment plans to generate new prostate intensity modulated radiation therapy (IMRT) plans. Each new case would be matched against others in the knowledge base. Once the best match is identified, that clinically approved plan is used to generate the new plan. METHODS: A database of 100 prostate IMRT treatment plans was assembled into an information-theoretic system. An algorithm based on mutual information was implemented to identify similar patient cases by matching 2D beam's eye view projections of contours. Ten randomly selected query cases were each matched with the most similar case from the database of prior clinically approved plans. Treatment parameters from the matched case were used to develop new treatment plans. A comparison of the differences in the dose-volume histograms between the new and the original treatment plans were analyzed. RESULTS: On average, the new knowledge-based plan is capable of achieving very comparable planning target volume coverage as the original plan, to within 2% as evaluated for D98, D95, and D1. Similarly, the dose to the rectum and dose to the bladder are also comparable to the original plan. For the rectum, the mean and standard deviation of the dose percentage differences for D20, D30, and D50 are 1.8% +/- 8.5%, -2.5% +/- 13.9%, and -13.9% +/- 23.6%, respectively. For the bladder, the mean and standard deviation of the dose percentage differences for D20, D30, and D50 are -5.9% +/- 10.8%, -12.2% +/- 14.6%, and -24.9% +/- 21.2%, respectively. A negative percentage difference indicates that the new plan has greater dose sparing as compared to the original plan. CONCLUSIONS: The authors demonstrate a knowledge-based approach of using prior clinically approved treatment plans to generate clinically acceptable treatment plans of high quality. This semiautomated approach has the potential to improve the efficiency of the treatment planning process while ensuring that high quality plans are developed. / Dissertation

La recherche sémantique de l'information a connu un nouvel essor avec les nouvelles technologies du Web sémantique. Des langages standards permettent aujourd'hui aux logiciels de communiquer par le biais de données écrites dans le vocabulaire d'ontologies de domaine décrivant une sémantique explicite. Cet accès ``sémantique'' à l'information requiert la disponibilité de bases de connaissances décrivant les instances des ontologies de domaine. Cependant, ces bases de connaissances, bien que de plus en plus riches, contiennent relativement peu d'information par comparaison au volume des informations contenu dans les documents du Web.La recherche sémantique de l'information atteint ainsi certaines limites par comparaison à la recherche classique de l'information qui exploite plus largement ces documents. Ces limites se traduisent explicitement par l'absence d'instances de concepts et de relations dans les bases de connaissances construites à partir des documents du Web. Dans cette thèse nous étudions deux directions de recherche différentes afin de permettre de répondre à des requêtes sémantiques dans de tels cas. Notre première étude porte sur la reformulation des requêtes sémantiques des utilisateurs afin d'atteindre des parties de document pertinentes à la place des faits recherchés et manquants dans les bases de connaissances. La deuxième problématique que nous étudions est celle de l'enrichissement des bases de connaissances par des instances de relations.Nous proposons deux solutions pour ces problématiques en exploitant des documents semi-structurés annotés par des concepts ou des instances de concepts. Un des points clés de ces solutions est qu'elles permettent de découvrir des instances de relations sémantiques sans s'appuyer sur des régularités lexico-syntaxiques ou structurelles dans les documents. Nous situons ces deux approches dans la littérature et nous les évaluons avec plusieurs corpus réels extraits du Web. Les résultats obtenus sur des corpus de citations bibliographiques, des corpus d'appels à communication et des corpus géographiques montrent que ces solutions permettent effectivement de retrouver de nouvelles instances relations à partir de documents hétérogènes tout en contrôlant efficacement leur précision. / Semantic information retrieval has known a rapid development with the new Semantic Web technologies. With these technologies, software can exchange and use data that are written according to domain ontologies describing explicit semantics. This ``semantic'' information access requires the availability of knowledge bases describing both domain ontologies and their instances. The most often, these knowledge bases are constructed automatically by annotating document corpora. However, while these knowledge bases are getting bigger, they still contain much less information when comparing them with the HTML documents available on the surface Web.Thus, semantic information retrieval reaches some limits with respect to ``classic'' information retrieval which exploits these documents at a bigger scale. In practice, these limits consist in the lack of concept and relation instances in the knowledge bases constructed from the same Web documents. In this thesis, we study two research directions in order to answer semantic queries in such cases. The first direction consists in reformulating semantic user queries in order to reach relevant document parts instead of the required (and missing) facts. The second direction that we study is the automatic enrichment of knowledge bases with relation instances.We propose two novel solutions for each of these research directions by exploiting semi-structured documents annotated with concept instances. A key point of these solutions is that they don't require lexico-syntactic or structure regularities in the documents. We position these approaches with respect to the state of the art and experiment them on several real corpora extracted from the Web. The results obtained from bibliographic citations, call-for-papers and geographic corpora show that these solutions allow to retrieve new answers/relation instances from heterogeneous documents and rank them efficiently according to their precision.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-16T18:09:36Z No. of bitstreams: 1 LuisEduardoSantos.pdf: 6383633 bytes, checksum: 7202e7e5051043d7889aea931f679cbe (MD5) / Made available in DSpace on 2017-08-16T18:09:36Z (GMT). No. of bitstreams: 1 LuisEduardoSantos.pdf: 6383633 bytes, checksum: 7202e7e5051043d7889aea931f679cbe (MD5) Previous issue date: 2013-01-29 / Application ontologies are knowledge representation structures capable of expressing a set of entities from a given domain, their relationships and axioms and has been used by modern Knowledge-Based Systems (KBS) in decision-making. Building application ontologies require a clear understanding of the goals of the KBS under development. Provide support for the formalization of these goals in an application ontology is the fundamental idea of GAODT. GAODT, the main contribution of this work, is a goal-oriented technique for building application ontologies. To provide support to its application a software tool named GAODTool was developed and used in an evaluation that consisted in building “InheritanceLaw”, an application ontology to support decision making in the domain of Intestate Succession, branch of law that governs the transfer of properties from someone after his death. / As ontologias de aplicação são estruturas de representação de conhecimento capazes de expressar um conjunto de entidades de um dado domínio, seus relacionamentos e axiomas, sendo utilizadas pelos modernos Sistemas Baseados em Conhecimento (SBC) no processo de tomada de decisões. Construir ontologias de aplicação exige o claro entendimento dos objetivos que o SBC pretende alcançar. Prover suporte sistematizado a formalização desses objetivos em uma ontologia de aplicação é a ideia fundamental da GAODT. Proposta nesse trabalho, a GAODT é uma técnica orientada por objetivos para a construção de ontologias de aplicação. Para prover suporte a sua aplicação foi desenvolvida a ferramenta de software GAODTool, utilizada na avaliação da GAODT através de um estudo de caso na construção da “InheritanceLaw”, uma ontologia de aplicação para apoio a tomada de decisões no domínio da Sucessão Legítima, ramo do direito que governa a transferência do patrimônio de alguém, depois de sua morte, em virtude de lei.

<p>This thesis presents the implementation of a knowledge base system for rough sets [Paw92]within the logic programming framework. The combination of rough set theory with logic programming is a novel approach. The presented implementation serves as a prototype system for the ideas presented in [VDM03a, VDM03b]. The system is available at "http://www.ida.liu.se/rkbs". </p><p>The presented language for describing knowledge in the rough knowledge base caters for implicit definition of rough sets by combining different regions (e.g. upper approximation, lower approximation, boundary) of other defined rough sets. The rough knowledge base system also provides methods for querying the knowledge base and methods for computing quantitative measures. </p><p>We test the implemented system on a medium sized application example to illustrate the usefulness of the system and the incorporated language. We also provide performance measurements of the system.</p>