Search (4 results, page 1 of 1)

  • × author_ss:"Mayr, P."
  • × author_ss:"Mutschke, P."
  • × year_i:[2010 TO 2020}
  1. Mutschke, P.; Mayr, P.: Science models for search : a study on combining scholarly information retrieval and scientometrics (2015) 0.00
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    Type
    a
  2. Mayr, P.; Mutschke, P.; Petras, V.; Schaer, P.; Sure, Y.: Applying science models for search (2010) 0.00
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    Abstract
    The paper proposes three different kinds of science models as value-added services that are integrated in the retrieval process to enhance retrieval quailty. The paper discusses the approaches Search Term Recommendation, Bradfordizing and Author Centrality on a general level and addresses implementation issues of the models within a real-life retrieval environment.
    Type
    a
  3. Mayr, P.; Schaer, P.; Mutschke, P.: ¬A science model driven retrieval prototype (2011) 0.00
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    Abstract
    This paper is about a better understanding of the structure and dynamics of science and the usage of these insights for compensating the typical problems that arises in metadata-driven Digital Libraries. Three science model driven retrieval services are presented: co-word analysis based query expansion, re-ranking via Bradfordizing and author centrality. The services are evaluated with relevance assessments from which two important implications emerge: (1) precision values of the retrieval services are the same or better than the tf-idf retrieval baseline and (2) each service retrieved a disjoint set of documents. The different services each favor quite other - but still relevant - documents than pure term-frequency based rankings. The proposed models and derived retrieval services therefore open up new viewpoints on the scientific knowledge space and provide an alternative framework to structure scholarly information systems.
    Type
    a
  4. Mayr, P.; Mutschke, P.; Schaer, P.; Sure, Y.: Mehrwertdienste für das Information Retrieval (2013) 0.00
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    Abstract
    Ziel des Projekts ist die Entwicklung und Erprobung von metadatenbasierten Mehr-wertdiensten für Retrievalumgebungen mit mehreren Datenbanken: a) Search Term Recommender (STR) als Dienst zum automatischen Vorschlagen von Suchbegriffen aus kontrollierten Vokabularen, b) Bradfordizing als Dienst zum Re-Ranking von Ergebnismengen nach Kernzeitschriften und c) Autorenzentralität als Dienst zum Re-Ranking von. Ergebnismengen nach Zentralität der Autoren in Autorennetzwerken. Schwerpunkt des Projektes ist die prototypische mplementierung der drei Mehrwertdienste in einer integrierten Retrieval-Testumgebung und insbesondere deren quantitative und qualitative Evaluation hinsichtlich Verbesserung der Retrievalqualität bei Einsatz der Mehrwertdienste.
    Type
    a