Submitted by Antonio Marin Neto (netomarin@gmail.com) on 2018-09-06T00:01:18Z No. of bitstreams: 1 Ontologias na representacao do conhecimeno.pdf: 1512033 bytes, checksum: 5cb40c53baa28e682303bb84ed68e480 (MD5) / Approved for entry into archive by Satie Tagara (satie@marilia.unesp.br) on 2018-09-06T16:36:29Z (GMT) No. of bitstreams: 1 marinneto_a_me_mar.pdf: 1512033 bytes, checksum: 5cb40c53baa28e682303bb84ed68e480 (MD5) / Made available in DSpace on 2018-09-06T16:36:29Z (GMT). No. of bitstreams: 1 marinneto_a_me_mar.pdf: 1512033 bytes, checksum: 5cb40c53baa28e682303bb84ed68e480 (MD5) Previous issue date: 2018-08-06 / Não recebi financiamento / As ontologias surgiram a partir de discussões na Filosofia com o objetivo de discutir a essência das coisas, nos estudos da metafísica por Aristóteles, que dentre os diferentes ramos de investigação filosófica, tratava do conhecimento da essência de toda a realidade. A ontologia trata do estudo do “Ser” enquanto ser. O termo foi posteriormente utilizado em diferentes áreas como a Ciência da Computação (CC), Psicologia, Ciência da Informação (CI), entre outros. Nesse texto é apresentado um levantamento da utilização das ontologias na representação do conhecimento, identificando e destacando a forma de utilização da ontologia como ferramenta na CC e CI. Para tal, o trabalho inicial com uma contextualização do termo ontologia, desde a sua origem na Filosofia até a sua utilização em algumas das áreas do conhecimento. Em seguida é apresentado um levantamento sobre a representação do conhecimento, a destacar a CC e CI, bem como sobre a utilização da ontologia nesse contexto informacional. Afim de evidenciar a importância das ontologias para representação do conhecimento, também são listadas as principais metodologias de criação de ontologia, aprendizado de ontologia, bem como as ferramentas utilizadas para essas tarefas, além do gerenciamento e engenharia de ontologias. E por fim são apresentadas as considerações finais acerca da utilização das ontologias como ferramentas semânticas na CI e como as futuruas pesquisas podem se beneficiar desse trabalho como uma base para o entendimento das ontologias como ferramenta semântica na CI. / Ontologies come from discussions in Philosophy to debate the essence of things in Aristotle's studies of metaphysics, which among the different branches of philosophical inquiry dealt with the knowledge of the essence of all reality. Ontology deals with the study of "Being" as being. The term was later used in different areas such as Computer Science (CC), Psychology, Information Science (IS), andothers. This thesis presents a survey of the use of ontologies in knowledge representation, identifying and highlighting how ontology is used as a semantic tool in CC and IS. For this, is presented a contextualization of the term ontology, from its origin in Philosophy to its use in some of the areas of knowledge. Next, a survey is presented on the representation of knowledge, highlighting the CC and IS, as well as on the use of the ontology in this informational context. In order to demonstrate the importance of ontologies for knowledge representation, the main methodologies for ontology creation, ontology learning, as well as the tools used for these tasks, as well as the management and engineering of ontologies are also listed. Finally, it's present the final considerations about the use of ontologies as semantic tools in IS and how future researches can benefit from this work as a basis for the understanding of ontologies as a semantic tool in IS.

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Made available in DSpace on 2015-03-05T13:59:42Z (GMT). No. of bitstreams: 0 Previous issue date: 21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O crescimento da Internet provoca a necessidade de estruturas mais consistentes de representação do conhecimento disponível na rede. Nesse contexto, a Web Semântica e as ontologias aparecem como resposta ao problema. Contudo, a construção de ontologias é extremamente custosa, o que estimula diversas pesquisas visando automatizar a tarefa. Em sua maioria, essas pesquisas partem do conhecimento disponível em textos. As ferramentas e métodos são, nesse caso, dependentes de idioma. Para que todos tenham acesso aos benefícios da utilização de ontologias em larga escala, estudos específicos para cada língua são necessários. Nesse sentido, pouco foi feito para o Português. Este trabalho procura avançar nas questões concernentes à tarefa para a língua portuguesa, abrangendo o desenvolvimento e a avaliação de métodos para a construção automática de ontologias a partir de textos. Além disso, foi desenvolvida uma ferramenta de auxílio à construção de ontologias para a língua portuguesa integrada ao ambiente largamente / The internet evolution is in need of more sophisticated knowledge management techniques. In this context, the Semantic Web and Ontologies are being developed as a way to solve this problem. Ontology learning is, however, a dificult and expensive task. Research on ontology learning is usually based on natural language texts. Language specific tools have to be developed. There is no much research that considers specifically the portuguese language. This work advances in these questions and it considers portuguese in particular. The development and evaluation of methods are presented and discussed. Besides, the developed methods were integrated as a plug-in of the widely used ontology editor Protégé

The advent of Massive Open Online Courses (MOOCs) allows massive volume of registrants to enrol in these MOOCs. This research aims to offer MOOCs registrants with automatic content related feedback to fulfil their cognitive needs. A framework is proposed which consists of three modules which are the subject ontology learning module, the short text classification module, and the question answering module. Unlike previous research, to identify relevant concepts for ontology learning a regular expression parser approach is used. Also, the relevant concepts are extracted from unstructured documents. To build the concept hierarchy, a frequent pattern mining approach is used which is guided by a heuristic function to ensure that sibling concepts are at the same level in the hierarchy. As this process does not require specific lexical or syntactic information, it can be applied to any subject. To validate the approach, the resulting ontology is used in a question-answering system which analyses students' content-related questions and generates answers for them. Textbook end of chapter questions/answers are used to validate the question-answering system. The resulting ontology is compared vs. the use of Text2Onto for the question-answering system, and it achieved favourable results. Finally, different indexing approaches based on a subject's ontology are investigated when classifying short text in MOOCs forum discussion data; the investigated indexing approaches are: unigram-based, concept-based and hierarchical concept indexing. The experimental results show that the ontology-based feature indexing approaches outperform the unigram-based indexing approach. Experiments are done in binary classification and multiple labels classification settings . The results are consistent and show that hierarchical concept indexing outperforms both concept-based and unigram-based indexing. The BAGGING and random forests classifiers achieved the best result among the tested classifiers.

La thèse est préparée dans le cadre d’une convention de cotutelle sous la direction des Professeurs Jean-Hugues Chauchat (ERIC-Lyon2) et N.V. Charonova (Université Nationale Polytechnique de Kharkov en Ukraine).1. Les résultats obtenus peuvent se résumer ainsi : Rétrospective des fondations théoriques sur la formalisation des connaissances et langue naturelle en tant que précurseurs de l’ingénierie des ontologies. Actualisation de l’état de l’art sur les approches générales dans le domaine de l’apprentissage d’ontologie, et sur les méthodes d’extraction des termes et des relations sémantiques. Panorama des plateformes et outils de construction et d’apprentissage des ontologies ; répertoire des ressources lexicales disponibles en ligne et susceptibles d’appuyer l’apprentissage d’ontologie (apprentissage des concepts et relation). 2. Propositions méthodologiques : Une méthode d’apprentissage des patrons morphosyntaxiques et d’installation de taxonomies partielles de termes. Une méthode de formation de classes sémantiques représentant les concepts et les relations pour le domaine de la sécurité radiologique. Un cadre (famework) d’organisation des étapes de travaux menant à la construction de l’ontologie du domaine de la sécurité radiologique.3. Implémentation et expérimentations : Installation de deux corpus spécialisés dans le domaine de la protection radiologique, en français et en russe, comprenant respectivement 1 500 000 et 600 000 unités lexicales. Implémentation des trois méthodes proposées et analyse des résultats obtenus. Les résultats ont été présentés dans 13 publications, revues et actes de conférences nationales et internationales, entre 2010 et 2016, notamment IMS-2012, TIA-2013, TOTH-2014, Eastern-European Journal of Eenterprise Technologies, Bionica Intellecta (Бионика интеллекта), Herald of the NTU «~KhPI~» (Вестник НТУ «~ХПИ~»). / The thesis has been prepared within a co-supervision agreement with the Professors Jean-Hugues Chauchat (ERIC-Lyon2) and N.V. Charonova (National Polytechnic University of Kharkov in Ukraine).The results obtained can be summarized as follows:1. State of the art:Retrospective of theoretical foundations concerning the formalization of knowledge and natural language as precursors of ontology engineering.Update of the state of the art on general approaches in the field of ontology learning, and on methods for extracting terms and semantic relations.Overview of platforms and tools for ontology construction and learning; list of lexical resources available online able to support ontology learning (concept learning and relationship).2. Methodological proposals:Learning morphosyntactic patterns and implementing partial taxonomies of terms.Finding semantic classes representing concepts and relationships for the field of radiological safety.Building a frame for the various stages of the work leading to the construction of the ontology in the field of radiological safety.3. Implementation and experiments:Loading of two corpuses specialized in radiological protection, in French and Russian, with 1,500,000 and 600,000 lexical units respectively.Implementation of the three previous methods and analysis of the results obtained.The results have been published in 13 national and international journals and proceedings, between 2010 and 2016, including IMS-2012, TIA-2013, TOTH-2014, Bionica Intellecta (Бионика интеллекта) , Herald of the NTU "~ KhPI ~" (Вестник НТУ "~ ХПИ ~").

Made available in DSpace on 2016-08-17T14:53:28Z (GMT). No. of bitstreams: 1 Suzane Carvalho dos Santos.pdf: 4549168 bytes, checksum: 85d08a343bc93d5bf241da9f6f02f5b4 (MD5) Previous issue date: 2014-08-18 / Ontologies are knowledge representation structures capable of expressing a set of entities of a domain, their relationships and axioms that are being used by modern knowledge based systems (KBS) in the decision making process. However, manual construction of ontology is expensive and subject to errors, thus a viable alternative is the automation of this process. Several techniques and tools have been developed to learn the different components of an ontology from textual sources, named concepts, hierarchies, instances, relationships, properties and axioms. However, these elements are generally acquired in a isolated manner. Due to the lack of approaches to acquire all the elements of an ontology jointly, there is a need to develop a process to make the reuse and the learning of each of the elements of an ontology in a synergistic manner. To attend this need, this work presents Apponto-Pro, an incremental learning process for populating application ontologies from textual information sources that is capable of generating a complete ontology through the integration of different techniques to generate isolated elements of an ontology. The process was evaluated through a case study that consisted in the automatic construction of Family_Law, an application ontology in the field of family law developed with Apponto-ProTool, a software tool to support Apponto-Pro that integrates the approaches that compound the whole process. This evaluation aimed to determine the effectiveness of the ontology constructed with Apponto-ProTool against an ontology manually built by a domain specialist and used as reference ontology. For this reason, the "precision"was calculated for the elements of the ontology automatically generated using the reference ontology. As a result it was found that in some cases the ontology developed with Apponto-ProTool tends to present more suitable results. / As ontologias 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. No entanto, a construção manual de ontologias é cara e sujeita a erros, sendo uma alternativa viável a sua construção de forma automática. Diversas técnicas e ferramentas têm sido desenvolvidas para aprender os diferentes componentes de uma ontologia a partir de fontes textuais, quais sejam conceitos, hierarquias, instâncias, relacionamentos, propriedades e axiomas. Entretanto estes elementos são, em regra, adquidiros de forma isolada. Devido à carência de abordagens que adquirem todos os elementos de uma ontologia de forma conjunta, surgiu a necessidade de desenvolver um processo que faça o reúso e a aprendizagem de cada um dos elementos de uma ontologia de forma completa. Atendendo a esta necessidade, este trabalho apresenta o Apponto-Pro, um processo incremental para o aprendizado e povoamento de ontologias de aplicação a partir de fontes de informação textuais capaz de gerar uma ontologia completa através da integração de diferentes técnicas que geram elementos da ontologia de forma isolada. O processo foi avalizado através de um estudo de caso que consistiu na construção automática da Family_Law, uma ontologia de aplicação no domínio do Direito da Família construída através da aplicação da ferramenta de software Apponto-ProTool, desenvolvida para dar suporte ao processo Apponto-Pro que integrou as ferramentas correspondentes as abordagens contidas no processo. Esta avaliação teve como objetivo verificar a efetividade da ontologia construída pela Apponto-ProTool em relação a uma ontologia construída manualmente por um especialista do domínio e utilizada como ontologia de referência. Para isso foi calculado o valor da medida "precision" para os elementos da ontologia construída utilizando a ontologia de referência. Como resultado verificou-se formalmente que em alguns casos a ontologia desenvolvida pela Apponto-ProTool tende a apresentar resultados mais adequados.

In computer science, an ontology is a machine-processable representation of knowledge about some domain. Ontologies are encoded in ontology languages, such as the Web Ontology Language (OWL) based on Description Logics (DLs). An ontology is a set of logical statements, called axioms. Some axioms make universal statements, e.g. all fathers are men, while others record data, i.e. facts about specific individuals, e.g. Bob is a father. A set of universal statements is called TBox, as it encodes terminology, i.e. schema-level conceptual relationships, and a set of facts is called ABox, as it encodes instance-level assertions. Ontologies are extensively developed and widely used in domains such as biology and medicine. Manual engineering of a TBox is a difficult task that includes modelling conceptual relationships of the domain and encoding those relationships in the ontology language, e.g. OWL. Hence, it requires the knowledge of domain experts and skills of ontology engineers combined together. In order to assist engineering of TBoxes and potentially automate it, acquisition (or induction) of axioms from data has attracted research attention and is usually called Ontology Learning (OL). This thesis investigates the problem of OL from general principles. We formulate it as General Terminology Induction that aims at acquiring general, expressive TBox axioms (called general terminology) from data. The thesis addresses and investigates in depth two main questions: how to rigorously evaluate the quality of general TBox axioms and how to efficiently construct them. We design an approach for General Terminology Induction and implement it in an algorithm called DL-Miner. We extensively evaluate DL-Miner, compare it with other approaches, and run case studies together with domain experts to gain insight into its potential applications. The thesis should be of interest to ontology developers seeking automated means to facilitate building or enriching ontologies. In addition, as our experiments show, DL-Miner can deliver valuable insights into the data, i.e. can be useful for data analysis and debugging.

La creciente necesidad de enriquecer la Web con grandes cantidades de ontologías que capturen el conocimiento del dominio ha generado multitud de estudios e investigaciones en metodologías para poder salvar el cuello de botella que supone la construcción manual de ontologías. Esta necesidad ha conducido a definir una nueva línea de investigación denominada Ontology Learning. La solución que proponemos en este trabajo se basa en el desarrollo de un nuevo entorno para extracción incremental de conocimiento desde texto en lenguaje natural. Se ha adoptado el punto de vista de la ingeniería ontológica, de modo que el conocimiento adquirido se representa por medio de ontologías. Este trabajo aporta un nuevo método para la construcción semiautomática de ontologías a partir de textos en lenguaje natural que no sólo se centra en la obtención de jerarquías de conceptos, sino que tiene en cuenta también un amplio conjunto de relaciones semánticas entre conceptos. / The need for enriching fue Web with large amounts of ontologies has increased. This need for domain models has generated several studies and research on methodologies capable of overcoming the bottleneck provoked by fue manual construction of ontologies. This need has led towards a new research area to obtain semiautomatic methods to build ontologies, which is called, Ontology Learning. The solution proposed in this work is based on the development of a new environment for incremental knowledge extraction from naturallanguage texts. F or this purpose, an ontological engineering perspective has been adopted. Hence, fue knowledge acquired through fue developed environment is represented by means of ontologies. This work presents a new method for fue semiautomatic construction of ontologies from naturallanguage texts. This method is not only based on obtaining hierarchies of concepts, but it uses a set of semantic relations between concepts.

Submitted by Cláudia Bueno (claudiamoura18@gmail.com) on 2015-10-21T20:58:05Z No. of bitstreams: 2 Dissertação - Norton Coelho Guimarães - 2015.pdf: 2090183 bytes, checksum: 2d7f73048d14bf0ac9fbbe295972b668 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-22T12:22:39Z (GMT) No. of bitstreams: 2 Dissertação - Norton Coelho Guimarães - 2015.pdf: 2090183 bytes, checksum: 2d7f73048d14bf0ac9fbbe295972b668 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-10-22T12:22:39Z (GMT). No. of bitstreams: 2 Dissertação - Norton Coelho Guimarães - 2015.pdf: 2090183 bytes, checksum: 2d7f73048d14bf0ac9fbbe295972b668 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2015-04-22 / The research on ontology learning has been carried out in various areas of knowledge. Semi-automatic or automatic extraction of ontologies would assist in the acceleration of knowledge structuring of multiple domains. Semi-automatic approaches to ontology learning from texts are proposed in several scientific papers, mostly with the support of natural language processing techniques. This paper describes the construction of a computational framework for semi-automated ontology learning from texts in the Portuguese language. Axioms are not dealt with in this paper. The work done here originated from the proposal of Philipp Cimiano [18], along with mechanisms for standardization of texts, Natural Language Processing, identification of taxonomic relationships and structure of ontologies. This research resulted in the development of a set of classes concrete and a set of abstract classes that comprise a computational framework. In this work we also present a case study in the field public safety, proving the benefits of computational framework. / As pesquisas sobre aprendizagem de ontologias têm sido realizadas em várias áreas do conhecimento. A extração semi-automática ou automática de ontologias auxiliaria na aceleração da estruturação do conhecimento de diversos domínios. Abordagens semiautomáticas para a aprendizagem de ontologias a partir de textos são propostas em diversos trabalhos científicos, em sua maioria, com o apoio de técnicas de processamento da língua natural. Este trabalho descreve a construção de um arcabouço computacional para aprendizagem semi-automatizada de ontologias a partir de textos na língua portuguesa. Axiomas não são tratados neste trabalho. O trabalho desenvolvido aqui originouse da proposta de Philipp Cimiano [18], juntamente com mecanismos de padronização de textos, processamento de linguagem natural, identificação de relações taxonômicas e estruturação de ontologias. Esta pesquisa resultou no desenvolvimento de um conjunto de classes, concretas e abstratas, que compõem um arcabouço computacional. Neste trabalho, também foi feito um estudo de caso no domínio de segurança pública, comprovando os benefícios do arcabouço computacional.

Made available in DSpace on 2016-08-17T14:53:16Z (GMT). No. of bitstreams: 1 Jone dos Santos Sodre Correa.pdf: 2272440 bytes, checksum: e8708cabafde69a2eb7580860867bc89 (MD5) Previous issue date: 2011-05-06 / Ontologies are an approach for knowledge representation capable of expressing a set of entities and their relationships, constraints, axioms and vocabulary of a given domain. Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semi-automatic approaches are needed. Ontology Learning looks for automatically or semi-automatically identifying ontology elements like classes, taxonomic and non-taxonomic relationships, properties and axioms from textual resources. This work proposes a process for automatic learning of ontologies from text focusing on the application of natural language processing techniques to acquire taxonomic relationships. Some experiments using a legal corpus were conducted in order to evaluate it. Initial results are promising. / Ontologias são uma forma de representação de conhecimento capaz de expressar um conjunto de entidades e suas relações, restrições, axiomas e vocabulário de um determinado domínio. A construção manual de ontologias por especialistas de domínio e engenheiros de conhecimento é uma tarefa cara e demorada e a automatização/semi-automatização desta tarefa é uma necessidade. O aprendizado de ontologias visa automatizar ou semi-automatizar a identificação de elementos de uma ontologia como classes, relações taxonômicas e não-taxonômicas, propriedades e axiomas de fontes textuais. Este trabalho propõe um processo de aprendizagem automática de ontologias a partir de fontes textuais enfocando a aplicação de técnicas de processamento de linguagem natural para adquirir relações taxonômicas. Alguns experimentos utilizando um corpus jurídico foram realizados para a avaliação da abordagem proposta. Os resultados iniciais são promissores.