Search (90 results, page 2 of 5)

  • × theme_ss:"Automatisches Indexieren"
  1. Gábor, K.; Zargayouna, H.; Tellier, I.; Buscaldi, D.; Charnois, T.: ¬A typology of semantic relations dedicated to scientific literature analysis (2016) 0.00 
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    Content
    Vortrag, "Semantics, Analytics, Visualisation: Enhancing Scholarly Data Workshop co-located with the 25th International World Wide Web Conference April 11, 2016 - Montreal, Canada", Montreal 2016.
  2. Panyr, J.: STEINADLER: ein Verfahren zur automatischen Deskribierung und zur automatischen thematischen Klassifikation (1978) 0.00 
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    Source
    Nachrichten für Dokumentation. 29(1978), S.92-96
  3. Salton, G.; Yang, C.S.: On the specification of term values in automatic indexing (1973) 0.00 
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    Source
    Journal of documentation. 29(1973), S.351-372
  4. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.00 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  5. Humphrey, S.M.: Automatic indexing of documents from journal descriptors : a preliminary investigation (1999) 0.00 
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    Abstract
    A new, fully automated approach for indedexing documents is presented based on associating textwords in a training set of bibliographic citations with the indexing of journals. This journal-level indexing is in the form of a consistent, timely set of journal descriptors (JDs) indexing the individual journals themselves. This indexing is maintained in journal records in a serials authority database. The advantage of this novel approach is that the training set does not depend on previous manual indexing of thousands of documents (i.e., any such indexing already in the training set is not used), but rather the relatively small intellectual effort of indexing at the journal level, usually a matter of a few thousand unique journals for which retrospective indexing to maintain consistency and currency may be feasible. If successful, JD indexing would provide topical categorization of documents outside the training set, i.e., journal articles, monographs, Web documents, reports from the grey literature, etc., and therefore be applied in searching. Because JDs are quite general, corresponding to subject domains, their most problable use would be for improving or refining search results
  6. Souza, R.R.; Raghavan, K.S.: ¬A methodology for noun phrase-based automatic indexing (2006) 0.00 
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    Abstract
    The scholarly community is increasingly employing the Web both for publication of scholarly output and for locating and accessing relevant scholarly literature. Organization of this vast body of digital information assumes significance in this context. The sheer volume of digital information to be handled makes traditional indexing and knowledge representation strategies ineffective and impractical. It is, therefore, worth exploring new approaches. An approach being discussed considers the intrinsic semantics of texts of documents. Based on the hypothesis that noun phrases in a text are semantically rich in terms of their ability to represent the subject content of the document, this approach seeks to identify and extract noun phrases instead of single keywords, and use them as descriptors. This paper presents a methodology that has been developed for extracting noun phrases from Portuguese texts. The results of an experiment carried out to test the adequacy of the methodology are also presented.
  7. Zhitomirsky-Geffet, M.; Prebor, G.; Bloch, O.: Improving proverb search and retrieval with a generic multidimensional ontology (2017) 0.00 
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    Abstract
    The goal of this research is to develop a generic ontological model for proverbs that unifies potential classification criteria and various characteristics of proverbs to enable their effective retrieval and large-scale analysis. Because proverbs can be described and indexed by multiple characteristics and criteria, we built a multidimensional ontology suitable for proverb classification. To evaluate the effectiveness of the constructed ontology for improving search and retrieval of proverbs, a large-scale user experiment was arranged with 70 users who were asked to search a proverb repository using ontology-based and free-text search interfaces. The comparative analysis of the results shows that the use of this ontology helped to substantially improve the search recall, precision, user satisfaction, and efficiency and to minimize user effort during the search process. A practical contribution of this work is an automated web-based proverb search and retrieval system which incorporates the proposed ontological scheme and an initial corpus of ontology-based annotated proverbs.
  8. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.00 
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    Date
    20.10.2000 12:22:23
  9. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.00 
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  10. Rädler, K.: In Bibliothekskatalogen "googlen" : Integration von Inhaltsverzeichnissen, Volltexten und WEB-Ressourcen in Bibliothekskataloge (2004) 0.00 
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  11. Smiraglia, R.P.; Cai, X.: Tracking the evolution of clustering, machine learning, automatic indexing and automatic classification in knowledge organization (2017) 0.00 
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    Abstract
    A very important extension of the traditional domain of knowledge organization (KO) arises from attempts to incorporate techniques devised in the computer science domain for automatic concept extraction and for grouping, categorizing, clustering and otherwise organizing knowledge using mechanical means. Four specific terms have emerged to identify the most prevalent techniques: machine learning, clustering, automatic indexing, and automatic classification. Our study presents three domain analytical case analyses in search of answers. The first case relies on citations located using the ISKO-supported "Knowledge Organization Bibliography." The second case relies on works in both Web of Science and SCOPUS. Case three applies co-word analysis and citation analysis to the contents of the papers in the present special issue. We observe scholars involved in "clustering" and "automatic classification" who share common thematic emphases. But we have found no coherence, no common activity and no social semantics. We have not found a research front, or a common teleology within the KO domain. We also have found a lively group of authors who have succeeded in submitting papers to this special issue, and their work quite interestingly aligns with the case studies we report. There is an emphasis on KO for information retrieval; there is much work on clustering (which involves conceptual points within texts) and automatic classification (which involves semantic groupings at the meta-document level).
  12. Ma, N.; Zheng, H.T.; Xiao, X.: ¬An ontology-based latent semantic indexing approach using long short-term memory networks (2017) 0.00 
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    Source
    Web and Big Data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7-9, 2017, Proceedings, Part I. Eds.: L. Chen et al
  13. Pintscher, L.; Bourgonje, P.; Moreno Schneider, J.; Ostendorff, M.; Rehm, G.: Wissensbasen für die automatische Erschließung und ihre Qualität am Beispiel von Wikidata : die Inhaltserschließungspolitik der Deutschen Nationalbibliothek (2021) 0.00 
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    Abstract
    Wikidata ist eine freie Wissensbasis, die allgemeine Daten über die Welt zur Verfügung stellt. Sie wird von Wikimedia entwickelt und betrieben, wie auch das Schwesterprojekt Wikipedia. Die Daten in Wikidata werden von einer großen Community von Freiwilligen gesammelt und gepflegt, wobei die Daten sowie die zugrundeliegende Ontologie von vielen Projekten, Institutionen und Firmen als Basis für Applikationen und Visualisierungen, aber auch für das Training von maschinellen Lernverfahren genutzt werden. Wikidata nutzt MediaWiki und die Erweiterung Wikibase als technische Grundlage der kollaborativen Arbeit an einer Wissensbasis, die verlinkte offene Daten für Menschen und Maschinen zugänglich macht. Ende 2020 beschreibt Wikidata über 90 Millionen Entitäten unter Verwendung von über 8 000 Eigenschaften, womit insgesamt mehr als 1,15 Milliarden Aussagen über die beschriebenen Entitäten getroffen werden. Die Datenobjekte dieser Entitäten sind mit äquivalenten Einträgen in mehr als 5 500 externen Datenbanken, Katalogen und Webseiten verknüpft, was Wikidata zu einem der zentralen Knotenpunkte des Linked Data Web macht. Mehr als 11 500 aktiv Editierende tragen neue Daten in die Wissensbasis ein und pflegen sie. Diese sind in Wiki-Projekten organisiert, die jeweils bestimmte Themenbereiche oder Aufgabengebiete adressieren. Die Daten werden in mehr als der Hälfte der Inhaltsseiten in den Wikimedia-Projekten genutzt und unter anderem mehr als 6,5 Millionen Mal am Tag über den SPARQL-Endpoint abgefragt, um sie in externe Applikationen und Visualisierungen einzubinden.
  14. Junger, U.: Möglichkeiten und Probleme automatischer Erschließungsverfahren in Bibliotheken : Bericht vom KASCADE-Workshop in der Universitäts- und Landesbibliothek Düsseldorf (1999) 0.00 
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    Date
    23.10.1996 17:26:29
  15. Spitters, M.J.: Adjust : automatische thesauriele ontsluiting van grote hoeveelheden krantenartikelen (1999) 0.00 
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    Source
    Informatie professional. 3(1999) no.10, S.29-31
  16. Knorz, G.: Automatische Indexierung (1994) 0.00 
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    Date
    29. 1.2011 17:56:21
  17. Nicoletti, M.: Automatische Indexierung (2001) 0.00 
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    Date
    29. 9.2017 12:00:04
  18. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.00 
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    Date
    14. 6.2015 22:12:44
  19. Hauer, M.: Automatische Indexierung (2000) 0.00 
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    Source
    Wissen in Aktion: Wege des Knowledge Managements. 22. Online-Tagung der DGI, Frankfurt am Main, 2.-4.5.2000. Proceedings. Hrsg.: R. Schmidt
  20. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.00 
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    Date
    14. 6.2015 22:12:56

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