Search (51 results, page 1 of 3)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.02 
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    Date
    11. 2.2011 18:22:58
  2. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.02 
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    Date
    11. 2.2011 18:22:25
  3. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.01 
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    Abstract
    The study reported here investigated the query expansion behavior of end-users interacting with a thesaurus-enhanced search system on the Web. Two groups, namely academic staff and postgraduate students, were recruited into this study. Data were collected from 90 searches performed by 30 users using the OVID interface to the CAB abstracts database. Data-gathering techniques included questionnaires, screen capturing software, and interviews. The results presented here relate to issues of search-topic and search-term characteristics, number and types of expanded queries, usefulness of thesaurus terms, and behavioral differences between academic staff and postgraduate students in their interaction. The key conclusions drawn were that (a) academic staff chose more narrow and synonymous terms than did postgraduate students, who generally selected broader and related terms; (b) topic complexity affected users' interaction with the thesaurus in that complex topics required more query expansion and search term selection; (c) users' prior topic-search experience appeared to have a significant effect on their selection and evaluation of thesaurus terms; (d) in 50% of the searches where additional terms were suggested from the thesaurus, users stated that they had not been aware of the terms at the beginning of the search; this observation was particularly noticeable in the case of postgraduate students.
    Date
    22. 7.2006 16:32:43
  4. Gillitzer, B.: Yewno (2017) 0.01 
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    Date
    22. 2.2017 10:16:49
  5. Heuss, T.; Humm, B.; Deuschel, T.; Frohlich, T.; Herth, T.; Mitesser, O.: Semantically guided, situation-aware literature research (2015) 0.01 
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  6. Horch, A.; Kett, H.; Weisbecker, A.: Semantische Suchsysteme für das Internet : Architekturen und Komponenten semantischer Suchmaschinen (2013) 0.01 
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    Abstract
    In der heutigen Zeit nimmt die Flut an Informationen exponentiell zu. In dieser »Informationsexplosion« entsteht täglich eine unüberschaubare Menge an neuen Informationen im Web: Beispielsweise 430 deutschsprachige Artikel bei Wikipedia, 2,4 Mio. Tweets bei Twitter und 12,2 Mio. Kommentare bei Facebook. Während in Deutschland vor einigen Jahren noch Google als nahezu einzige Suchmaschine beim Zugriff auf Informationen im Web genutzt wurde, nehmen heute die u.a. in Social Media veröffentlichten Meinungen und damit die Vorauswahl sowie Bewertung von Informationen einzelner Experten und Meinungsführer an Bedeutung zu. Aber wie können themenspezifische Informationen nun effizient für konkrete Fragestellungen identifiziert und bedarfsgerecht aufbereitet und visualisiert werden? Diese Studie gibt einen Überblick über semantische Standards und Formate, die Prozesse der semantischen Suche, Methoden und Techniken semantischer Suchsysteme, Komponenten zur Entwicklung semantischer Suchmaschinen sowie den Aufbau bestehender Anwendungen. Die Studie erläutert den prinzipiellen Aufbau semantischer Suchsysteme und stellt Methoden der semantischen Suche vor. Zudem werden Softwarewerkzeuge vorgestellt, mithilfe derer einzelne Funktionalitäten von semantischen Suchmaschinen realisiert werden können. Abschließend erfolgt die Betrachtung bestehender semantischer Suchmaschinen zur Veranschaulichung der Unterschiede der Systeme im Aufbau sowie in der Funktionalität.
  7. Robertson, S.E.: On term selection for query expansion (1990) 0.01 
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    Abstract
    In the framework of a relevance feedback system, term values or term weights may be used to (a) select new terms for inclusion in a query, and/or (b) weight the terms for retrieval purposes once selected. It has sometimes been assumed that the same weighting formula should be used for both purposes. This paper sketches a quantitative argument which suggests that the two purposes require different weighting formulae
  8. Deuschel, T.; Heuss, T.; Humm, B.: ¬Die Medienplattform : ein System für gerichtete und ungerichtete semantische Suchen (2015) 0.01 
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  9. Jansen, B.; Browne, G.M.: Navigating information spaces : index / mind map / topic map? (2021) 0.01 
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  10. Hetzler, B.: Visual analysis and exploration of relationships (2002) 0.01 
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  11. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.01 
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
  12. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.01 
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    Date
    30. 3.2001 13:32:22
  13. Rahmstorf, G.: Integriertes Management inhaltlicher Datenarten (2001) 0.01 
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    Abstract
    Inhaltliche Daten sind im Unterschied zu Messdaten, Zahlen, Analogsignalen und anderen Informationen solche Daten, die sich auch sprachlich interpretieren lassen. Sie transportieren Inhalte, die sich benennen lassen. Zu inhaltlichen Daten gehören z. B. Auftragsdaten, Werbetexte, Produktbezeichnungen und Patentklassifikationen. Die meisten Daten, die im Internet kommuniziert werden, sind inhaltliche Daten. Man kann inhaltliche Daten in vier Klassen einordnen: * Wissensdaten - formatierte Daten (Fakten u. a. Daten in strukturierter Form), - nichtformatierte Daten (vorwiegend Texte); * Zugriffsdaten - Benennungsdaten (Wortschatz, Terminologie, Themen u. a.), - Begriffsdaten (Ordnungs- und Bedeutungsstrukturen). In der Wissensorganisation geht es hauptsächlich darum, die unüberschaubare Fülle des Wissens zu ordnen und wiederauffindbar zu machen. Daher befasst sich das Fach nicht nur mit dem Wissen selbst, selbst sondern auch mit den Mitteln, die dazu verwendet werden, das Wissen zu ordnen und auffindbar zu machen
  14. Arenas, M.; Cuenca Grau, B.; Kharlamov, E.; Marciuska, S.; Zheleznyakov, D.: Faceted search over ontology-enhanced RDF data (2014) 0.01 
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  15. Selvaretnam, B.; Belkhatir, M.: ¬A linguistically driven framework for query expansion via grammatical constituent highlighting and role-based concept weighting (2016) 0.01 
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  16. Wongthontham, P.; Abu-Salih, B.: Ontology-based approach for semantic data extraction from social big data : state-of-the-art and research directions (2018) 0.01 
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  17. Mandalka, M.: Open semantic search zum unabhängigen und datenschutzfreundlichen Erschliessen von Dokumenten (2015) 0.01 
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    Content
    Unabhängiges Durchsuchen und Analysieren grosser Datenmengen Damit können investigativ arbeitende Journalisten selbstständig und auf eigener Hardware datenschutzfreundlich hunderte, tausende, hunderttausende oder gar Millionen von Dokumenten oder hunderte Megabyte, Gigabytes oder gar einige Terabytes an Daten mit Volltextsuche durchsuchbar machen. Automatische Datenanreicherung und Erschliessung mittels Hintergrundwissen Zudem wird anhand von konfigurierbaren Hintergrundwissen automatisch eine interaktive Navigation zu in Dokumenten enthaltenen Namen von Bundestagsabgeordneten oder Orten in Deutschland generiert oder anhand Textmustern strukturierte Informationen wie Geldbeträge extrahiert. Mittels Named Entities Manager für Personen, Organisationen, Begriffe und Orte können eigene Rechercheschwerpunkte konfiguriert werden, aus denen dann automatisch eine interaktive Navigation (Facettensuche) und aggregierte Übersichten generiert werden. Automatische Datenvisualisierung Diese lassen sich auch visualisieren: So z.B. die zeitliche Verteilung von Suchergebnissen als Trand Diagramm oder durch gleichzeitige Nennung in Dokumenten abgeleitete Verbindungen als Netzwerk bzw. Graph.
  18. Chen, H.; Lally, A.M.; Zhu, B.; Chau, M.: HelpfulMed : Intelligent searching for medical information over the Internet (2003) 0.00 
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  19. Koike, A.; Takagi, T.: Knowledge discovery based on an implicit and explicit conceptual network (2007) 0.00 
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    Abstract
    The amount of knowledge accumulated in published scientific papers has increased due to the continuing progress being made in scientific research. Since numerous papers have only reported fragments of scientific facts, there are possibilities for discovering new knowledge by connecting these facts. We therefore developed a system called BioTermNet to draft a conceptual network with hybrid methods of information extraction and information retrieval. Two concepts are regarded as related in this system if (a) their relationship is clearly described in MEDLINE abstracts or (b) they have distinctively co-occurred in abstracts. PRIME data, including protein interactions and functions extracted by NLP techniques, are used in the former, and the Singhalmeasure for information retrieval is used in the latter. Relationships that are not clearly or directly described in an abstract can be extracted by connecting multiple concepts. To evaluate how well this system performs, Swanson's association between Raynaud's disease and fish oil and that between migraine and magnesium were tested with abstracts that had been published before the discovery of these associations. The result was that when start and end concepts were given, plausible and understandable intermediate concepts connecting them could be detected. When only the start concept was given, not only the focused concept (magnesium and fish oil) but also other probable concepts could be detected as related concept candidates. Finally, this system was applied to find diseases related to the BRCA1 gene. Some other new potentially related diseases were detected along with diseases whose relations to BRCA1 were already known.
  20. Symonds, M.; Bruza, P.; Zuccon, G.; Koopman, B.; Sitbon, L.; Turner, I.: Automatic query expansion : a structural linguistic perspective (2014) 0.00 
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