Search (4 results, page 1 of 1)

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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

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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

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Abstract

Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.

Type

a

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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