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  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Zarri, G.P.: Utilisation de structures conceptuelles complexes pour représenter le "contenu sémantique" de textes narratifs (1999) 0.06
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    Imprint
    Lille : Université Charles-de-Gaulle
    Pages
    S.89-99
    Source
    Organisation des connaissances en vue de leur intégration dans les systèmes de représentation et de recherche d'information. Ed.: J. Maniez, et al
  2. Brandão, W.C.; Santos, R.L.T.; Ziviani, N.; Moura, E.S. de; Silva, A.S. da: Learning to expand queries using entities (2014) 0.05
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    Date
    22. 8.2014 17:07:50
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1870-1883
  3. Hannech, A.: Système de recherche d'information étendue basé sur une projection multi-espaces (2018) 0.05
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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.
    Since its appearance in the early 90's, the World Wide Web (WWW or Web) has provided universal access to knowledge and the world of information has been primarily witness to a great revolution (the digital revolution). It quickly became very popular, making it the largest and most comprehensive database and knowledge base thanks to the amount and diversity of data it contains. However, the considerable increase and evolution of these data raises important problems for users, in particular for accessing the documents most relevant to their search queries. In order to cope with this exponential explosion of data volume and facilitate their access by users, various models are offered by information retrieval systems (IRS) for the representation and retrieval of web documents. Traditional SRIs use simple keywords that are not semantically linked to index and retrieve these documents. This creates limitations in terms of the relevance and ease of exploration of results. To overcome these limitations, existing techniques enrich documents by integrating external keywords from different sources. However, these systems still suffer from limitations that are related to the exploitation techniques of these sources of enrichment. When the different sources are used so that they cannot be distinguished by the system, this limits the flexibility of the exploration models that can be applied to the results returned by this system. Users then feel lost to these results, and find themselves forced to filter them manually to select the relevant information. If they want to go further, they must reformulate and target their search queries even more until they reach the documents that best meet their expectations. In this way, even if the systems manage to find more relevant results, their presentation remains problematic. In order to target research to more user-specific information needs and improve the relevance and exploration of its research findings, advanced SRIs adopt different data personalization techniques that assume that current research of user is directly related to his profile and / or previous browsing / search experiences.
    Footnote
    Thèse de doctorat, Université du Québec à Chicoutimi.
    Pages
    351 S
  4. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.03
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    Date
    30. 3.2001 13:32:22
    Source
    Computer journal. 26(1983), S.239-246
  5. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.03
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
    Pages
    S.145-159
  6. Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016) 0.03
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    Pages
    S.262-277
    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  7. Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016) 0.02
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    Date
    1. 2.2016 18:25:22
    Pages
    S.141-152
  8. Kozikowski, P. et al.: Support of part-whole relations in query answering (2016) 0.02
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    Date
    1. 2.2016 18:25:22
    Pages
    S.94-107
  9. Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016) 0.02
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    Pages
    S.249-261
    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  10. Colace, F.; Santo, M. De; Greco, L.; Napoletano, P.: Weighted word pairs for query expansion (2015) 0.02
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    Source
    Information processing and management. 51(2015) no.1, S.179-193
  11. Sacco, G.M.: Dynamic taxonomies and guided searches (2006) 0.02
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    Date
    22. 7.2006 17:56:22
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.6, S.792-796
  12. Bräscher, M.: Semantic relations in knowledge organization systems (2014) 0.02
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    Footnote
    Papers from the 2nd ISKO-Brazil Conference, Rio de Janeiro, May, 2013.
    Source
    Knowledge organization. 41(2014) no.2, S.175-180
  13. Colace, F.; Santo, M. de; Greco, L.; Napoletano, P.: Improving relevance feedback-based query expansion by the use of a weighted word pairs approach (2015) 0.02
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.11, S.2223-2234
  14. Efthimiadis, E.N.: End-users' understanding of thesaural knowledge structures in interactive query expansion (1994) 0.02
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    Date
    30. 3.2001 13:35:22
    Pages
    S.295-303
  15. Semantic search over the Web (2012) 0.02
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    Abstract
    The Web has become the world's largest database, with search being the main tool that allows organizations and individuals to exploit its huge amount of information. Search on the Web has been traditionally based on textual and structural similarities, ignoring to a large degree the semantic dimension, i.e., understanding the meaning of the query and of the document content. Combining search and semantics gives birth to the idea of semantic search. Traditional search engines have already advertised some semantic dimensions. Some of them, for instance, can enhance their generated result sets with documents that are semantically related to the query terms even though they may not include these terms. Nevertheless, the exploitation of the semantic search has not yet reached its full potential. In this book, Roberto De Virgilio, Francesco Guerra and Yannis Velegrakis present an extensive overview of the work done in Semantic Search and other related areas. They explore different technologies and solutions in depth, making their collection a valuable and stimulating reading for both academic and industrial researchers. The book is divided into three parts. The first introduces the readers to the basic notions of the Web of Data. It describes the different kinds of data that exist, their topology, and their storing and indexing techniques. The second part is dedicated to Web Search. It presents different types of search, like the exploratory or the path-oriented, alongside methods for their efficient and effective implementation. Other related topics included in this part are the use of uncertainty in query answering, the exploitation of ontologies, and the use of semantics in mashup design and operation. The focus of the third part is on linked data, and more specifically, on applying ideas originating in recommender systems on linked data management, and on techniques for the efficiently querying answering on linked data.
    Editor
    Virgilio, R. de
    Pages
    XX, 423 S
  16. Bergamaschi, S.; Domnori, E.; Guerra, F.; Rota, S.; Lado, R.T.; Velegrakis, Y.: Understanding the semantics of keyword queries on relational data without accessing the instance (2012) 0.02
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    Pages
    S.131-158
    Source
    Semantic search over the Web. Eds.: R. De Virgilio, et al
  17. Meij, E.; Rijke, M. de: Thesaurus-based feedback to support mixed search and browsing environments (2007) 0.02
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    Pages
    S.247-258
  18. Zenz, G.; Zhou, X.; Minack, E.; Siberski, W.; Nejdl, W.: Interactive query construction for keyword search on the Semantic Web (2012) 0.02
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    Pages
    S.109-130
    Source
    Semantic search over the Web. Eds.: R. De Virgilio, et al
  19. Gnoli, C.; Santis, R. de; Pusterla, L.: Commerce, see also Rhetoric : cross-discipline relationships as authority data for enhanced retrieval (2015) 0.02
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    Pages
    S.151-162
  20. Cai, F.; Rijke, M. de: Learning from homologous queries and semantically related terms for query auto completion (2016) 0.02
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    Source
    Information processing and management. 52(2016) no.4, S.628-643

Years

Languages

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  • chi 1
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Types

  • a 224
  • m 19
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