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  1. Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999) 0.17 
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    Date
    17. 7.2002 19:22:06
    RSWK
    Data mining / Lehrbuch
    Subject
    Data mining / Lehrbuch
    Theme
    Data Mining
  2. Priss, U.: Description logic and faceted knowledge representation (1999) 0.07 
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    Abstract
    The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.
    Date
    22. 1.2016 17:30:31
  3. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.07 
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    Source
    http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
  4. Winterhalter, C.: Licence to mine : ein Überblick über Rahmenbedingungen von Text and Data Mining und den aktuellen Stand der Diskussion (2016) 0.06 
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    Abstract
    Der Artikel gibt einen Überblick über die Möglichkeiten der Anwendung von Text and Data Mining (TDM) und ähnlichen Verfahren auf der Grundlage bestehender Regelungen in Lizenzverträgen zu kostenpflichtigen elektronischen Ressourcen, die Debatte über zusätzliche Lizenzen für TDM am Beispiel von Elseviers TDM Policy und den Stand der Diskussion über die Einführung von Schrankenregelungen im Urheberrecht für TDM zu nichtkommerziellen wissenschaftlichen Zwecken.
    Theme
    Data Mining
  5. Junger, U.; Schwens, U.: ¬Die inhaltliche Erschließung des schriftlichen kulturellen Erbes auf dem Weg in die Zukunft : Automatische Vergabe von Schlagwörtern in der Deutschen Nationalbibliothek (2017) 0.06 
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    Abstract
    Wir leben im 21. Jahrhundert, und vieles, was vor hundert und noch vor fünfzig Jahren als Science Fiction abgetan worden wäre, ist mittlerweile Realität. Raumsonden fliegen zum Mars, machen dort Experimente und liefern Daten zur Erde zurück. Roboter werden für Routineaufgaben eingesetzt, zum Beispiel in der Industrie oder in der Medizin. Digitalisierung, künstliche Intelligenz und automatisierte Verfahren sind kaum mehr aus unserem Alltag wegzudenken. Grundlage vieler Prozesse sind lernende Algorithmen. Die fortschreitende digitale Transformation ist global und umfasst alle Lebens- und Arbeitsbereiche: Wirtschaft, Gesellschaft und Politik. Sie eröffnet neue Möglichkeiten, von denen auch Bibliotheken profitieren. Der starke Anstieg digitaler Publikationen, die einen wichtigen und prozentual immer größer werdenden Teil des Kulturerbes darstellen, sollte für Bibliotheken Anlass sein, diese Möglichkeiten aktiv aufzugreifen und einzusetzen. Die Auswertbarkeit digitaler Inhalte, beispielsweise durch Text- and Data-Mining (TDM), und die Entwicklung technischer Verfahren, mittels derer Inhalte miteinander vernetzt und semantisch in Beziehung gesetzt werden können, bieten Raum, auch bibliothekarische Erschließungsverfahren neu zu denken. Daher beschäftigt sich die Deutsche Nationalbibliothek (DNB) seit einigen Jahren mit der Frage, wie sich die Prozesse bei der Erschließung von Medienwerken verbessern und maschinell unterstützen lassen. Sie steht dabei im regelmäßigen kollegialen Austausch mit anderen Bibliotheken, die sich ebenfalls aktiv mit dieser Fragestellung befassen, sowie mit europäischen Nationalbibliotheken, die ihrerseits Interesse an dem Thema und den Erfahrungen der DNB haben. Als Nationalbibliothek mit umfangreichen Beständen an digitalen Publikationen hat die DNB auch Expertise bei der digitalen Langzeitarchivierung aufgebaut und ist im Netzwerk ihrer Partner als kompetente Gesprächspartnerin geschätzt.
    Date
    19. 8.2017 9:24:22
  6. Perovsek, M.; Kranjca, J.; Erjaveca, T.; Cestnika, B.; Lavraca, N.: TextFlows : a visual programming platform for text mining and natural language processing (2016) 0.06 
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    Abstract
    Text mining and natural language processing are fast growing areas of research, with numerous applications in business, science and creative industries. This paper presents TextFlows, a web-based text mining and natural language processing platform supporting workflow construction, sharing and execution. The platform enables visual construction of text mining workflows through a web browser, and the execution of the constructed workflows on a processing cloud. This makes TextFlows an adaptable infrastructure for the construction and sharing of text processing workflows, which can be reused in various applications. The paper presents the implemented text mining and language processing modules, and describes some precomposed workflows. Their features are demonstrated on three use cases: comparison of document classifiers and of different part-of-speech taggers on a text categorization problem, and outlier detection in document corpora.
  7. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.05 
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  8. Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006) 0.05 
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    Source
    Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]
  9. Hensinger, P.: Trojanisches Pferd "Digitale Bildung" : Auf dem Weg zur Konditionierungsanstalt in einer Schule ohne Lehrer? (2017) 0.05 
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    Abstract
    Wir hatten schon viele Schulreformen, und nun wird von der Kultusministerkonferenz eine weitere angekün-digt, die "Digitale Bildung": Unterricht mit digitalen Medien wie Smartphone und Tablet-PC über WLAN.1Medien und Bildungspolitiker predigen Eltern, ihre Kinder seien in Schule und Beruf chancenlos, wenn sie nicht schon in der Grundschule Apps programmieren lernen.Die Hauptinitiative der Digitalisierung der Bildung kommt von der IT-Branche. Im Zwischenbericht der Platt-form "Digitalisierung in Bildung und Wissenschaft" steht, wer das Bundeswissenschaftsministerium berät - nämlich Akteure der IT-Wirtschaft: Vom Bitkom, der Gesellschaft für Informatik (GI) über Microsoft, SAP bis zur Telekom sind alle vertreten (BUNDESMINISTERIUM 2016:23). Nicht vertreten dagegen sind Kinderärzte, Päda-gogen, Lernpsychologen oder Neurowissenschaftler, die sich mit den Folgen der Nutzung von Bildschirm-medien bei Kindern und Jugendlichen beschäftigen. Die New York Times schlägt in einer Analyse Alarm: "How Google Took Over the Classroom" (13.05.2017).2 Mit ausgeklügelten Methoden, den Hype um digitale Medien nutzend, greift Google nach der Kontrolle des US-Bildungswesens, auch der Kontrolle über die Inhalte. Wer bei der Analyse und Bewertung dieser Entwicklung nur fragt "Nützen digitale Medien im Unterricht?", verengt den Blick, reduziert auf Methodik und Didaktik und schließt Gesamtzusammenhänge aus. Denn die digitalen Medien sind mehr als nur Unterrichts-Hilfsmittel. Diesen Tunnelblick weitet die IT-Unternehmerin Yvonne Hofstetter. Sie schreibt in ihrem Buch "Das Ende der Demokratie": "Mit der Digitalisierung verwandeln wir unser Leben, privat wie beruflich, in einen Riesencomputer. Alles wird gemessen, gespeichert, analysiert und prognostiziert, um es anschließend zu steuern und zu optimieren"(HOFSTETTER 2016:37). Grundlage dafür ist das Data-Mining - das Sammeln von Daten - für BigData Analysen. Die Haupt-Schürfwerkzeuge dazu sind dasSmartphone, der TabletPC und das WLAN-Netz.
    Date
    22. 2.2019 11:45:19
  10. Jäger, L.: Von Big Data zu Big Brother (2018) 0.05 
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    Date
    22. 1.2018 11:33:49
    Theme
    Data Mining
  11. Mohr, J.W.; Bogdanov, P.: Topic models : what they are and why they matter (2013) 0.04 
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    Abstract
    We provide a brief, non-technical introduction to the text mining methodology known as "topic modeling." We summarize the theory and background of the method and discuss what kinds of things are found by topic models. Using a text corpus comprised of the eight articles from the special issue of Poetics on the subject of topic models, we run a topic model on these articles, both as a way to introduce the methodology and also to help summarize some of the ways in which social and cultural scientists are using topic models. We review some of the critiques and debates over the use of the method and finally, we link these developments back to some of the original innovations in the field of content analysis that were pioneered by Harold D. Lasswell and colleagues during and just after World War II.
    Theme
    Data Mining
  12. Eckert, K: ¬The ICE-map visualization (2011) 0.04 
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    Abstract
    In this paper, we describe in detail the Information Content Evaluation Map (ICE-Map Visualization, formerly referred to as IC Difference Analysis). The ICE-Map Visualization is a visual data mining approach for all kinds of concept hierarchies that uses statistics about the concept usage to help a user in the evaluation and maintenance of the hierarchy. It consists of a statistical framework that employs the the notion of information content from information theory, as well as a visualization of the hierarchy and the result of the statistical analysis by means of a treemap.
  13. Bauckhage, C.: Moderne Textanalyse : neues Wissen für intelligente Lösungen (2016) 0.04 
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    Theme
    Data Mining
  14. Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016) 0.04 
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    Theme
    Data Mining
  15. Nohr, H.: Big Data im Lichte der EU-Datenschutz-Grundverordnung (2017) 0.04 
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    Theme
    Data Mining
  16. Shala, E.: ¬Die Autonomie des Menschen und der Maschine : gegenwärtige Definitionen von Autonomie zwischen philosophischem Hintergrund und technologischer Umsetzbarkeit (2014) 0.03 
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    Footnote
    Vgl. unter: https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwizweHljdbcAhVS16QKHXcFD9QQFjABegQICRAB&url=https%3A%2F%2Fwww.researchgate.net%2Fpublication%2F271200105_Die_Autonomie_des_Menschen_und_der_Maschine_-_gegenwartige_Definitionen_von_Autonomie_zwischen_philosophischem_Hintergrund_und_technologischer_Umsetzbarkeit_Redigierte_Version_der_Magisterarbeit_Karls&usg=AOvVaw06orrdJmFF2xbCCp_hL26q.
  17. Dietz, K.: en.wikipedia.org > 6 Mio. Artikel (2020) 0.03 
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    Content
    "Die Englischsprachige Wikipedia verfügt jetzt über mehr als 6 Millionen Artikel. An zweiter Stelle kommt die deutschsprachige Wikipedia mit 2.3 Millionen Artikeln, an dritter Stelle steht die französischsprachige Wikipedia mit 2.1 Millionen Artikeln (via Researchbuzz: Firehose <https://rbfirehose.com/2020/01/24/techcrunch-wikipedia-now-has-more-than-6-million-articles-in-english/> und Techcrunch <https://techcrunch.com/2020/01/23/wikipedia-english-six-million-articles/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&guccounter=1&guce_referrer=aHR0cHM6Ly9yYmZpcmVob3NlLmNvbS8yMDIwLzAxLzI0L3RlY2hjcnVuY2gtd2lraXBlZGlhLW5vdy1oYXMtbW9yZS10aGFuLTYtbWlsbGlvbi1hcnRpY2xlcy1pbi1lbmdsaXNoLw&guce_referrer_sig=AQAAAK0zHfjdDZ_spFZBF_z-zDjtL5iWvuKDumFTzm4HvQzkUfE2pLXQzGS6FGB_y-VISdMEsUSvkNsg2U_NWQ4lwWSvOo3jvXo1I3GtgHpP8exukVxYAnn5mJspqX50VHIWFADHhs5AerkRn3hMRtf_R3F1qmEbo8EROZXp328HMC-o>). 250120 via digithek ch = #fineBlog s.a.: Angesichts der Veröffentlichung des 6-millionsten Artikels vergangene Woche in der englischsprachigen Wikipedia hat die Community-Zeitungsseite "Wikipedia Signpost" ein Moratorium bei der Veröffentlichung von Unternehmensartikeln gefordert. Das sei kein Vorwurf gegen die Wikimedia Foundation, aber die derzeitigen Maßnahmen, um die Enzyklopädie gegen missbräuchliches undeklariertes Paid Editing zu schützen, funktionierten ganz klar nicht. *"Da die ehrenamtlichen Autoren derzeit von Werbung in Gestalt von Wikipedia-Artikeln überwältigt werden, und da die WMF nicht in der Lage zu sein scheint, dem irgendetwas entgegenzusetzen, wäre der einzige gangbare Weg für die Autoren, fürs erste die Neuanlage von Artikeln über Unternehmen zu untersagen"*, schreibt der Benutzer Smallbones in seinem Editorial <https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost/2020-01-27/From_the_editor> zur heutigen Ausgabe."
  18. Shen, M.; Liu, D.-R.; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies (2012) 0.03 
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    Abstract
    Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is- part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.
  19. Maaten, L. van den: Learning a parametric embedding by preserving local structure (2009) 0.03 
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    Theme
    Data Mining
  20. Kipcic, O.; Cramer, C.: Wie Zeitungsinhalte Forschung und Entwicklung befördern (2017) 0.03 
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    Theme
    Data Mining

Authors

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