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  • × theme_ss:"Wissensrepräsentation"
  1. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.06
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    Date
    13.12.2017 14:17:22
  2. Panzer, M.: Dewey Web services : overview (2009) 0.04
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  3. OWL Web Ontology Language Test Cases (2004) 0.04
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    Date
    14. 8.2011 13:33:22
    Editor
    Carroll, J.J. u. J. de Roo
  4. Panzer, M.: Ontological representation of the DDC : Brainstorming, May 6th 2008 (2008) 0.03
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  5. Almeida Campos, M.L. de; Machado Campos, M.L.; Dávila, A.M.R.; Espanha Gomes, H.; Campos, L.M.; Lira e Oliveira, L. de: Information sciences methodological aspects applied to ontology reuse tools : a study based on genomic annotations in the domain of trypanosomatides (2013) 0.03
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    Date
    22. 2.2013 12:03:53
  6. Dobrev, P.; Kalaydjiev, O.; Angelova, G.: From conceptual structures to semantic interoperability of content (2007) 0.03
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    Source
    Conceptual structures: knowledge architectures for smart applications: 15th International Conference on Conceptual Structures, ICCS 2007, Sheffield, UK, July 22 - 27, 2007 ; proceedings. Eds.: U. Priss u.a
  7. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.03
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    Date
    16.11.2018 14:22:01
  8. Gödert, W.; Hubrich, J.; Nagelschmidt, M.: Semantic knowledge representation for information retrieval (2014) 0.03
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    Date
    23. 7.2017 13:49:22
    Imprint
    Berlin : De Gruyter Saur
  9. Schöndorf, P.: Nicht-konventionelle Thesaurusrelationen als Orientierungshilfen für Indexierung und Recherche: Analyse ausgewählter Beispiele (1988) 0.03
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  10. Hitzler, P.; Janowicz, K.: Ontologies in a data driven world : finding the middle ground (2013) 0.03
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  11. Baião Salgado Silva, G.; Lima, G.Â. Borém de Oliveira: Using topic maps in establishing compatibility of semantically structured hypertext contents (2012) 0.03
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    Date
    22. 2.2013 11:39:23
  12. Hannech, A.: Système de recherche d'information étendue basé sur une projection multi-espaces (2018) 0.02
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    Abstract
    Depuis son apparition au début des années 90, le World Wide Web (WWW ou Web) a offert un accès universel aux connaissances et le monde de l'information a été principalement témoin d'une grande révolution (la révolution numérique). Il est devenu rapidement très populaire, ce qui a fait de lui la plus grande et vaste base de données et de connaissances existantes grâce à la quantité et la diversité des données qu'il contient. Cependant, l'augmentation et l'évolution considérables de ces données soulèvent d'importants problèmes pour les utilisateurs notamment pour l'accès aux documents les plus pertinents à leurs requêtes de recherche. Afin de faire face à cette explosion exponentielle du volume de données et faciliter leur accès par les utilisateurs, différents modèles sont proposés par les systèmes de recherche d'information (SRIs) pour la représentation et la recherche des documents web. Les SRIs traditionnels utilisent, pour indexer et récupérer ces documents, des mots-clés simples qui ne sont pas sémantiquement liés. Cela engendre des limites en termes de la pertinence et de la facilité d'exploration des résultats. Pour surmonter ces limites, les techniques existantes enrichissent les documents en intégrant des mots-clés externes provenant de différentes sources. Cependant, ces systèmes souffrent encore de limitations qui sont liées aux techniques d'exploitation de ces sources d'enrichissement. Lorsque les différentes sources sont utilisées de telle sorte qu'elles ne peuvent être distinguées par le système, cela limite la flexibilité des modèles d'exploration qui peuvent être appliqués aux résultats de recherche retournés par ce système. Les utilisateurs se sentent alors perdus devant ces résultats, et se retrouvent dans l'obligation de les filtrer manuellement pour sélectionner l'information pertinente. S'ils veulent aller plus loin, ils doivent reformuler et cibler encore plus leurs requêtes de recherche jusqu'à parvenir aux documents qui répondent le mieux à leurs attentes. De cette façon, même si les systèmes parviennent à retrouver davantage des résultats pertinents, leur présentation reste problématique. Afin de cibler la recherche à des besoins d'information plus spécifiques de l'utilisateur et améliorer la pertinence et l'exploration de ses résultats de recherche, les SRIs avancés adoptent différentes techniques de personnalisation de données qui supposent que la recherche actuelle d'un utilisateur est directement liée à son profil et/ou à ses expériences de navigation/recherche antérieures. Cependant, cette hypothèse ne tient pas dans tous les cas, les besoins de l'utilisateur évoluent au fil du temps et peuvent s'éloigner de ses intérêts antérieurs stockés dans son profil.
    Dans d'autres cas, le profil de l'utilisateur peut être mal exploité pour extraire ou inférer ses nouveaux besoins en information. Ce problème est beaucoup plus accentué avec les requêtes ambigües. Lorsque plusieurs centres d'intérêt auxquels est liée une requête ambiguë sont identifiés dans le profil de l'utilisateur, le système se voit incapable de sélectionner les données pertinentes depuis ce profil pour répondre à la requête. Ceci a un impact direct sur la qualité des résultats fournis à cet utilisateur. Afin de remédier à quelques-unes de ces limitations, nous nous sommes intéressés dans ce cadre de cette thèse de recherche au développement de techniques destinées principalement à l'amélioration de la pertinence des résultats des SRIs actuels et à faciliter l'exploration de grandes collections de documents. Pour ce faire, nous proposons une solution basée sur un nouveau concept d'indexation et de recherche d'information appelé la projection multi-espaces. Cette proposition repose sur l'exploitation de différentes catégories d'information sémantiques et sociales qui permettent d'enrichir l'univers de représentation des documents et des requêtes de recherche en plusieurs dimensions d'interprétations. L'originalité de cette représentation est de pouvoir distinguer entre les différentes interprétations utilisées pour la description et la recherche des documents. Ceci donne une meilleure visibilité sur les résultats retournés et aide à apporter une meilleure flexibilité de recherche et d'exploration, en donnant à l'utilisateur la possibilité de naviguer une ou plusieurs vues de données qui l'intéressent le plus. En outre, les univers multidimensionnels de représentation proposés pour la description des documents et l'interprétation des requêtes de recherche aident à améliorer la pertinence des résultats de l'utilisateur en offrant une diversité de recherche/exploration qui aide à répondre à ses différents besoins et à ceux des autres différents utilisateurs. Cette étude exploite différents aspects liés à la recherche personnalisée et vise à résoudre les problèmes engendrés par l'évolution des besoins en information de l'utilisateur. Ainsi, lorsque le profil de cet utilisateur est utilisé par notre système, une technique est proposée et employée pour identifier les intérêts les plus représentatifs de ses besoins actuels dans son profil. Cette technique se base sur la combinaison de trois facteurs influents, notamment le facteur contextuel, fréquentiel et temporel des données. La capacité des utilisateurs à interagir, à échanger des idées et d'opinions, et à former des réseaux sociaux sur le Web, a amené les systèmes à s'intéresser aux types d'interactions de ces utilisateurs, au niveau d'interaction entre eux ainsi qu'à leurs rôles sociaux dans le système. Ces informations sociales sont abordées et intégrées dans ce travail de recherche. L'impact et la manière de leur intégration dans le processus de RI sont étudiés pour améliorer la pertinence des résultats.
    Footnote
    Thèse de doctorat, Université du Québec à Chicoutimi.
  13. RDF Semantics (2004) 0.02
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    Editor
    Hayes, P. u. B. McBride
  14. Oliveira Lima, G.A.B. de: Hypertext model - HTXM : a model for hypertext organization of documents (2008) 0.02
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    Content
    This article reports an applied research on the construction and implementation of a semantically structured conceptual prototype to help in the organization and representation of human knowledge in hypertextual systems, based on four references: the Facet Analysis Theory (FAT), the Conceptual Map Theory, semantic structure of hypertext links and the technical guidelines of the Associacao Brasileira de Normas Técnicas (ABNT). This prototype, called Modelo Hipertextual para Organizacao de Documentos (MHTX) - Model For Hypertext Organization of Documents HTXM - is formed by a semantic structure called Conceptual Map (CM) and Expanded Summary (ES), the latter based on the summary of a selected doctoral thesis to which access points were designed. In the future, this prototype maybe used to implement a digital libraty called BTDECI - UFMG (Biblioteca de Teses e Dissertacöes do Programa de Pós-Graduacao da Escola de Ciência da Informacao da UFMG - Library of Theses and Dissertations of the Graduate Program of School of Information Science of Universidade Federal de Minas Gerais).
  15. Stuart, D.: Practical ontologies for information professionals (2016) 0.02
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    Content
    C H A P T E R 1 What is an ontology?; Introduction; The data deluge and information overload; Defining terms; Knowledge organization systems and ontologies; Ontologies, metadata and linked data; What can an ontology do?; Ontologies and information professionals; Alternatives to ontologies; The aims of this book; The structure of this book; C H A P T E R 2 Ontologies and the semantic web; Introduction; The semantic web and linked data; Resource Description Framework (RDF); Classes, subclasses and properties; The semantic web stack; Embedded RDF; Alternative semantic visionsLibraries and the semantic web; Other cultural heritage institutions and the semantic web; Other organizations and the semantic web; Conclusion; C H A P T E R 3 Existing ontologies; Introduction; Ontology documentation; Ontologies for representing ontologies; Ontologies for libraries; Upper ontologies; Cultural heritage data models; Ontologies for the web; Conclusion; C H A P T E R 4 Adopting ontologies; Introduction; Reusing ontologies: application profiles and data models; Identifying ontologies; The ideal ontology discovery tool; Selection criteria; Conclusion C H A P T E R 5 Building ontologiesIntroduction; Approaches to building an ontology; The twelve steps; Ontology development example: Bibliometric Metrics Ontology element set; Conclusion; C H A P T E R 6 Interrogating ontologies; Introduction; Interrogating ontologies for reuse; Interrogating a knowledge base; Understanding ontology use; Conclusion; C H A P T E R 7 The future of ontologies and the information professional; Introduction; The future of ontologies for knowledge discovery; The future role of library and information professionals; The practical development of ontologies
  16. Almeida, M.B.: Ontologia em Ciência da Informação: Teoria e Método (1ª ed., Vol. 1). CRV. http://dx.doi.org/10.24824/978655578679.8; Tecnologia e Aplicações (1ª ed., Vol. 2). CRV. http://dx.doi.org/10.24824/978652511477.4; Curso completo com teoria e exercícios (1ª ed., volume suplementar para professores). CRV. [Review] (2022) 0.02
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    Abstract
    Nos últimos 30 anos, o tema das ontologias tem sido um dos terrenos mais férteis de investigação na área da Organização do Conhecimento. É um tema complexo e revestido de polémica, pela dificuldade na definição do próprio conceito e pelas apropriações que diferentes campos científicos têm exercido sobre ele. Com origem no domínio da filosofia, a ontologia é hoje um território partilhado pelas Ciências da Computação, com destaque para a Ciência dos Dados (Data Science), e pela Ciência da Informação, particularmente pela Organização do Conhecimento. São raros os autores desta área que não escreveram sobre o tema, abordando as suas fronteiras conceptuais ou discutindo a relação das ontologias com outros sistemas de organização do conhecimento, como as taxonomias, os tesauros ou as classificações.
    Source
    Boletim do Arquivo da Universidade de Coimbra 35(2022) no.1, S.191-198
  17. Bloehdorn, S.; Cimiano, P.; Duke, A.; Haase, P.; Heizmann, J.; Thurlow, I.; Völker, J.: Ontology-based question answering for digital libraries (2007) 0.02
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  18. Muljarto, A.-R.; Salmon, J.-M.; Neveu, P.; Charnomordic, B.; Buche, P.: Ontology-based model for food transformation processes : application to winemaking (2014) 0.02
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  19. Tramullas, J.; Garrido-Picazo, P.; Sánchez-Casabón, A.I.: Use of Wikipedia categories on information retrieval research : a brief review (2020) 0.02
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    Type
    p
  20. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.02
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

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