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  1. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.85
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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
  2. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.58
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
    Source
    WWW '02: Proceedings of the 11th International Conference on World Wide Web, May 7-11, 2002, Honolulu, Hawaii, USA
  3. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.58
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  4. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.47
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  5. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.41
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    Abstract
    In this thesis we propose three new word association measures for multi-word term extraction. We combine these association measures with LocalMaxs algorithm in our extraction model and compare the results of different multi-word term extraction methods. Our approach is language and domain independent and requires no training data. It can be applied to such tasks as text summarization, information retrieval, and document classification. We further explore the potential of using multi-word terms as an effective representation for general web-page summarization. We extract multi-word terms from human written summaries in a large collection of web-pages, and generate the summaries by aligning document words with these multi-word terms. Our system applies machine translation technology to learn the aligning process from a training set and focuses on selecting high quality multi-word terms from human written summaries to generate suitable results for web-page summarization.
    Content
    A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.
    Date
    10. 1.2013 19:22:47
  6. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.41
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    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  7. Farazi, M.: Faceted lightweight ontologies : a formalization and some experiments (2010) 0.36
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    Abstract
    While classifications are heavily used to categorize web content, the evolution of the web foresees a more formal structure - ontology - which can serve this purpose. Ontologies are core artifacts of the Semantic Web which enable machines to use inference rules to conduct automated reasoning on data. Lightweight ontologies bridge the gap between classifications and ontologies. A lightweight ontology (LO) is an ontology representing a backbone taxonomy where the concept of the child node is more specific than the concept of the parent node. Formal lightweight ontologies can be generated from their informal ones. The key applications of formal lightweight ontologies are document classification, semantic search, and data integration. However, these applications suffer from the following problems: the disambiguation accuracy of the state of the art NLP tools used in generating formal lightweight ontologies from their informal ones; the lack of background knowledge needed for the formal lightweight ontologies; and the limitation of ontology reuse. In this dissertation, we propose a novel solution to these problems in formal lightweight ontologies; namely, faceted lightweight ontology (FLO). FLO is a lightweight ontology in which terms, present in each node label, and their concepts, are available in the background knowledge (BK), which is organized as a set of facets. A facet can be defined as a distinctive property of the groups of concepts that can help in differentiating one group from another. Background knowledge can be defined as a subset of a knowledge base, such as WordNet, and often represents a specific domain.
    Content
    PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.
  8. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.35
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    Footnote
    Vgl.: http://ieeexplore.ieee.org/Xplore/login.jsp?reload=true&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4755313%2F4755314%2F04755480.pdf%3Farnumber%3D4755480&authDecision=-203.
  9. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.35
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  10. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.35
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  11. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.32
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    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
  12. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.29
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    Source
    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  13. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.28
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    Abstract
    By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.
    Content
    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
    Theme
    Semantic Web
  14. Heckner, M.: Tagging, rating, posting : studying forms of user contribution for web-based information management and information retrieval (2009) 0.09
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    BK
    05.38 / Neue elektronische Medien <Kommunikationswissenschaft>
    Classification
    05.38 / Neue elektronische Medien <Kommunikationswissenschaft>
    Content
    The Web of User Contribution - Foundations and Principles of the Social Web - Social Tagging - Rating and Filtering of Digital Resources Empirical Analysisof User Contributions - The Functional and Linguistic Structure of Tags - A Comparative Analysis of Tags for Different Digital Resource Types - Exploring Relevance Assessments in Social IR Systems - Exploring User Contribution Within a Higher Education Scenario - Summary of Empirical Results and Implications for Designing Social Information Systems User Contribution for a Participative Information System - Social Information Architecture for an Online Help System
    Object
    Web 2.0
    RSWK
    World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
    Social Tagging / Filter / Web log / World Wide Web 2.0
    Subject
    World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
    Social Tagging / Filter / Web log / World Wide Web 2.0
  15. Reasoning Web : Semantic Interoperability on the Web, 13th International Summer School 2017, London, UK, July 7-11, 2017, Tutorial Lectures (2017) 0.07
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    Abstract
    This volume contains the lecture notes of the 13th Reasoning Web Summer School, RW 2017, held in London, UK, in July 2017. In 2017, the theme of the school was "Semantic Interoperability on the Web", which encompasses subjects such as data integration, open data management, reasoning over linked data, database to ontology mapping, query answering over ontologies, hybrid reasoning with rules and ontologies, and ontology-based dynamic systems. The papers of this volume focus on these topics and also address foundational reasoning techniques used in answer set programming and ontologies.
    Content
    Neumaier, Sebastian (et al.): Data Integration for Open Data on the Web - Stamou, Giorgos (et al.): Ontological Query Answering over Semantic Data - Calì, Andrea: Ontology Querying: Datalog Strikes Back - Sequeda, Juan F.: Integrating Relational Databases with the Semantic Web: A Reflection - Rousset, Marie-Christine (et al.): Datalog Revisited for Reasoning in Linked Data - Kaminski, Roland (et al.): A Tutorial on Hybrid Answer Set Solving with clingo - Eiter, Thomas (et al.): Answer Set Programming with External Source Access - Lukasiewicz, Thomas: Uncertainty Reasoning for the Semantic Web - Calvanese, Diego (et al.): OBDA for Log Extraction in Process Mining
    RSWK
    Ontologie <Wissensverarbeitung> / Semantic Web
    Series
    Lecture Notes in Computer Scienc;10370 )(Information Systems and Applications, incl. Internet/Web, and HCI
    Subject
    Ontologie <Wissensverarbeitung> / Semantic Web
    Theme
    Semantic Web
  16. Wester, J.: AutoFocus: An Open-source Facet-Driven Enterprise Search Solution (2007) 0.05
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    Abstract
    In the final presentation of the afternoon, Jeroen Wester of Aduna described the main features of their open-source, facet-driven enterprise search solution, AutoFocus. AutoFocus is based upon and exploits the advantages of Semantic Web technologies, in particular RDF (Resource Description Framework), although a bewildering variety of related technologies - XML, SOAP, SKOS, OWL - are also employed. In addition to providing components for metadata-based data integration and cross-silo search and navigation in a single enterprise search solution, AutoFocus offers the advantage of being open-source, meaning that its source code is freely available for customization
    Theme
    Semantic Web
  17. Web intelligence: research and development : First Asia-Pacific Conference, WI 2001, Maebashi City, Japan, Oct. 23-26, 2001, Proceedings (2003) 0.05
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    Abstract
    Web Intelligence is a new research area dealing with all aspects of intelligent data processing in the Web. As the ferst book devoted to Web Intelligence, this coherently written multi-author monograph provides a thorough introduction and systematic overview of this new area. It presents both the current state of research as well as application aspects. All major topics related to Web Intelligence which can have impact an future directions and developments are presented in detail. This book will be a valuable source of reference for years to all research and development professionals interested in Weh Intelligence. Students will also appreciate the numerous illustrations and examples.
    Footnote
    Rez. in: nfd - Information 54(2003) H.6, S.378-379 (T. Mandl): "Im Oktober 2001 fand erstmals eine Tagung mit dem Titel "Web Intelligence" statt. Ist dies nun eine neue Disziplin oder der Versuch analog zu "Artificial Intelligence" und "Computational Intelligence" ein neues Modewort zu kreieren? Geht es um den Einsatz sogenannter intelligenter Verfahren, um mit dem Internet umgehen zu können oder erscheint das Internet als "emerging global brain" (Goertzel 2002), also als eine unerschöpfliche Quelle von Wissen, die nur geschickt ausgebeutet werden muss? Kommt also die Intelligenz aus dem Web oder dient die Intelligenz als Werkzeug für das Web? Der Tagungsband ist seit Anfang 2003 verfügbar und bietet nun den Anlass, diesen Begriff anhand der darin präsentierten Inhalte zu bewerten. Die Herausgeber führen in ihrem einleitenden Artikel gleich die Abkürzung WI ein und versuchen tatsächlich "Web Intelligence" als neue Sub-Disziplin der Informatik zu etablieren. Zu diesem Zweck greifen sie auch auf die Anzahl der Nachweise für diese Phrase in Suchmaschinen zu. Zwar lieferten die Systeme angeblich Zahlen von über einer Million (S. 4), aber dies überzeugt sicher noch niemanden, das Studium der WI aufzunehmen. Allerdings weist dieses Vorgehen schon auf einen Kern der WI hin: man versucht, aus dem im Web gespeicherten Wissen neues Wissen zu generieren. Damit wäre man sehr nahe am Data oder eben Web-Mining, jedoch geht die Definition der Autoren darüber hinaus. Sie wollen WI verstanden wissen als die Anwendung von Künstlicher Intelligenz sowie Informationstechnologie im Internet (S. 2). Da nun Künstliche Intelligenz bei allen Meinungsverschiedenheiten sicherlich nicht ohne Informationstechnologie denkbar ist, wirkt die Definition nicht ganz überzeugend. Allerdings beschwichtigen die Autoren im gleichen Atemzug und versichern, diese Definition solle ohnehin keine Forschungsrichtung ausschließen. Somit bietet sich eher eine Umfangsdefinition an. Diese solle die wichtigsten Stoßrichtungen des Buchs und damit auch der Tagung umfassen. Als Ausgangspunkt dient dazu auch eine Liste der Herausgeber (S. 7f.), die hier aber etwas modifiziert wird: - Grundlagen von Web Informationssystemen (Protokolle, Technologien, Standards) - Web Information Retrieval, WebMining und Farming - Informationsmanagement unter WebBedingungen - Mensch-Maschine Interaktion unter Web-Bedingungen (hier "HumanMedia Engineering" S. XII) Eine grobe Einteilung wie diese ist zwar übersichtlich, führt aber zwangsläufig zu Ouerschnittsthemen. In diesem Fall zählt dazu das Semantic Web, an dem momentan sehr intensiv geforscht wird. Das Semantic Web will das Unbehagen mit der Anarchie im Netz und daraus folgenden Problemen für die Suchmaschinen überwinden, indem das gesamte Wissen im Web auch explizit als solches gekennzeichnet wird. Tauchen auf einer WebSeite zwei Namen auf und einer ist der des Autors und der andere der eines Sponsors, so erlauben neue Technologien, diese auch als solche zu bezeichnen. Noch wichtiger, wie in einer Datenbank sollen nun Abfragen möglich sein, welche andere Seiten aus dem Web liefen, die z.B. den gleichen Sponsor, aber einen anderen Autor haben. Dieser Thematik widmen sich etwa Hendler & Feigenbaum. Das Semantic Web stellt ein Ouerschnittsthema dar, da dafür neue Technologien (Mizoguchi) und ein neuartiges Informationsmanagement erforderlich sind (z.B. Stuckenschmidt & van Harmelen), die Suchverfahren angepasst werden und natürlich auch auf die Benutzer neue Herausforderungen zukommen. Diesem Aspekt, inwieweit Benutzer solche Anfragen überhaupt stellen werden, widmet sich in diesem Band übrigens niemand ernsthaft. Im Folgenden sollen die einzelnen Themengebiete anhand der im Band enthaltenen Inhalte näher bestimmt werden, bevor abschließend der Versuch eines Resümees erfolgt.
    - Grundlagen von Web Informationssystemen Protokolle, Technologien und Standards existieren inzwischen mannigfaltig und lediglich für spezifische Anwendungen entstehen weitere Grundlagen. In dem vorliegenden Band gibt es etwa ein Datenmodell für XML-Datenbanken (Wuwongse et al.) und den Vorschlag einer 3DModellierung (Hwang, Lee & Hwang). Auch für Proxy-Server werden neue Algorithmen entwickelt (Aguilar & Leiss). - Web Information Retrieval, WebMining und Farming Neben klassischen Themen des Information Retrieval wie kontrolliertem Vokabular (Sim & Wong), Ranking (Wang & Maguire), Kategorisierung (Loia & Luongo) und Term-Erweiterung (Huang, Oyang & Chien) stehen auch typische Web Information Retrieval Themen. Multimedia Retrieval spielt eine wichtige Rolle im Web und dazu gibt es Beiträge zu Audio (Wieczorkowska & Ra- Wan, Liu & Wang) und Grafiken (Fukumoto & Cho, Hwang, Lee & Hwang). Das Hype-Thema Link-Analyse schlägt auch gleich den Bogen hin zum Web-Mining, ist mit fünf Beiträgen aber eher unterrepräsentiert. Link-Analyse stellt die Frage, was sich aus den inzwischen wohl über zehn Milliarden Links im Internet folgern lässt. So extrahieren zwei Beiträge die zeitliche Veränderung sozialer Strukturen in Web Communities. Matsumura et al. untersuchen, ob Außenseiter sich auch für die innerhalb einer Community diskutierten Themen interessieren und werten dies als Maß für die Verbreitung des Themas. Bun & Ishizuka interessieren sich nur für die Änderungen innerhalb einer Gruppe von thematisch zusammengehörigen Web-Abgeboten und analysieren in diesem Korpus die wichtigsten Sätze, die neu entstehende Themen am besten repräsentieren. Andere Mining-Beiträge befassen sich mit der Erstellung von Sprachressourcen (Chau & Yeh). - Informationsmanagement unter WebBedingungen Für das Informationsmanagement gelten Ontologien zur Beschreibung des vorhandenen Wissens als wichtiges Instrument und dementsprechend ist "Ontologie" auch ein Kandidat für das höchst-frequente Wort in dem Tagungsband.
    Einen weiteren wichtigen Aspekt stellt nach wie vor E-Learning dar, das u.a. neue Anforderungen an die Erstellung und Verwaltung von Lernmodulen (Forcheri et al.) und die Zusammenarbeit von Lehrern und Schülern stellt (Hazeyama et al., Liu et al.). - Mensch-Maschine Interaktion unter Web-Bedingungen Benutzermodellierung (Estivill-Castro & Yang, Lee, Sung & Cho) hat mit der Popularität des Internet eine neue Dimension gewonnen und ist besonders im kommerziellen Umfeld sehr interessant. Eine Wissensquelle hierfür und für andere Anwendungen sind Log-Files (Yang et al.). Breiten Raum nehmen die Visualisierungen ein, die häufig für spezielle Benutzergruppen gedacht sind, wie etwa Data Mining Spezialisten (Han & Cercone) und Soziologen, die sich mit Web-Communities befassen (Sumi & Mase). Agenten (Lee) und Assistenten (Molina) als neue Formen der Interaktion treten nicht zuletzt für E-Commerce Anwendungen in Erscheinung. In diesem Kontext der Mensch-Medien-Beziehung soll das Ouerschnittsthema WebCommunities genannt werden, in dem die sozialen Aspekte der Kooperation (Hazeyama et al.) ebenso wie das Entdecken von Gruppenstrukturen (Bun & Ishizuka) untersucht werden. Dagegen kommen kaum empirische Evaluierungen vor, die belegen könnten, wie intelligent denn die Systeme nun sind. Worin liegt nun der Kern der Web Intelligence? Der Aspekt Web Mining befasst sich mit der Extraktion von Wissen aus dem riesigen Reservoir Internet während der Aspekt Web-Informationssysteme den Einsatz so genannter intelligenter Technologien in Informationssystemen im Internet behandelt. Da jedoch das Spektrum der eingesetzten Informationssysteme praktisch beliebig ist und auch die Auswahl der intelligenten Technologien keinen spezifischen Fokus erkennen lässt, stellt Web Intelligence momentan eher einen bunten Strauß dar als ein klar abgegrenztes Feld. Das Web taugt inzwischen kaum mehr zur Abgrenzung von Technologien. Die Beiträge sind stärker von den Communities der Autoren geprägt als von der Web Intelligence Community, die vielleicht noch gar nicht existiert. Wenn doch, so befindet sie sich in einem frühen Stadium, in dem sich wenig Gemeinsamkeiten zwischen den Arbeiten erkennen lassen. Allerdings macht die mangelnde Kohärenz die einzelnen Beiträge keineswegs uninteressant. Dieser Meinung sind offensichtlich auch die 57 Mitglieder des Programmkomitees der Tagung, unter denen auch drei deutsche Wissenschaftler sind. Denn für 2003 ist eine weitere Tagung geplant (http://www.comp.hkbu. edu.hk/WIo3/)."
  18. Farkas, M.G.: Social software in libraries : building collaboration, communication, and community online (2007) 0.05
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    RSWK
    Bibliothek / Wiki
    Bibliothek / Web log
    Subject
    Bibliothek / Wiki
    Bibliothek / Web log
  19. Wusteman, J.: Patently ridiculous (2004) 0.05
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    Abstract
    The Open Source Software movement has much to offer the library community. But can it survive the onslaught of patent applications?
    Source
    Library hi tech. 22(2004) no.2, S.231-237
  20. Ozmutlu, H.C.; Cavdur, F.; Ozmutlu, S.: Cross-validation of neural network applications for automatic new topic identification (2008) 0.05
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    Abstract
    The purpose of this study is to provide results from experiments designed to investigate the cross-validation of an artificial neural network application to automatically identify topic changes in Web search engine user sessions by using data logs of different Web search engines for training and testing the neural network. Sample data logs from the FAST and Excite search engines are used in this study. The results of the study show that identification of topic shifts and continuations on a particular Web search engine user session can be achieved with neural networks that are trained on a different Web search engine data log. Although FAST and Excite search engine users differ with respect to some user characteristics (e.g., number of queries per session, number of topics per session), the results of this study demonstrate that both search engine users display similar characteristics as they shift from one topic to another during a single search session. The key finding of this study is that a neural network that is trained on a selected data log could be universal; that is, it can be applicable on all Web search engine transaction logs regardless of the source of the training data log.

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