Search (209 results, page 1 of 11)

  • × theme_ss:"Wissensrepräsentation"
  • × year_i:[2010 TO 2020}
  1. 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) 0.06
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
    Language
    e
  2. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.04
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    Date
    13.12.2017 14:17:22
  3. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.04
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    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
    Language
    e
  4. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.04
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    Date
    16.11.2018 14:22:01
    Language
    e
  5. Hitzler, P.; Janowicz, K.: Ontologies in a data driven world : finding the middle ground (2013) 0.03
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  6. 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
    Language
    e
  7. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.02
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    Classification
    JZN (E)
    TVV (E)
    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
    GHBS
    JZN (E)
    TVV (E)
    Language
    e
  8. 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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    Language
    e
  9. Helbig, H.: Knowledge representation and the semantics of natural language (2014) 0.02
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    Classification
    P
    Language
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    LCC
    P
  10. Deokattey, S.; Neelameghan, A.; Kumar, V.: ¬A method for developing a domain ontology : a case study for a multidisciplinary subject (2010) 0.02
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    Date
    22. 7.2010 19:41:16
    Language
    e
  11. Madalli, D.P.; Balaji, B.P.; Sarangi, A.K.: Music domain analysis for building faceted ontological representation (2014) 0.02
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    Language
    e
    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  12. Prud'hommeaux, E.; Gayo, E.: RDF ventures to boldly meet your most pedestrian needs (2015) 0.02
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    e
    Source
    Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22
  13. Wunner, T.; Buitelaar, P.; O'Riain, S.: Semantic, terminological and linguistic interpretation of XBRL (2010) 0.02
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  14. Mohr, J.W.; Bogdanov, P.: Topic models : what they are and why they matter (2013) 0.02
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  19. Gödert, W.; Hubrich, J.; Nagelschmidt, M.: Semantic knowledge representation for information retrieval (2014) 0.01
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    Date
    23. 7.2017 13:49:22
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  20. Fernández, M.; Cantador, I.; López, V.; Vallet, D.; Castells, P.; Motta, E.: Semantically enhanced Information Retrieval : an ontology-based approach (2011) 0.01
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    Abstract
    Currently, techniques for content description and query processing in Information Retrieval (IR) are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. Aiming to solve the limitations of keyword-based models, the idea of conceptual search, understood as searching by meanings rather than literal strings, has been the focus of a wide body of research in the IR field. More recently, it has been used as a prototypical scenario (or even envisioned as a potential "killer app") in the Semantic Web (SW) vision, since its emergence in the late nineties. However, current approaches to semantic search developed in the SW area have not yet taken full advantage of the acquired knowledge, accumulated experience, and technological sophistication achieved through several decades of work in the IR field. Starting from this position, this work investigates the definition of an ontology-based IR model, oriented to the exploitation of domain Knowledge Bases to support semantic search capabilities in large document repositories, stressing on the one hand the use of fully fledged ontologies in the semantic-based perspective, and on the other hand the consideration of unstructured content as the target search space. The major contribution of this work is an innovative, comprehensive semantic search model, which extends the classic IR model, addresses the challenges of the massive and heterogeneous Web environment, and integrates the benefits of both keyword and semantic-based search. Additional contributions include: an innovative rank fusion technique that minimizes the undesired effects of knowledge sparseness on the yet juvenile SW, and the creation of a large-scale evaluation benchmark, based on TREC IR evaluation standards, which allows a rigorous comparison between IR and SW approaches. Conducted experiments show that our semantic search model obtained comparable and better performance results (in terms of MAP and P@10 values) than the best TREC automatic system.
    Language
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Authors

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