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  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.32 
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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
  2. Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002) 0.08 
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    Abstract
    Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.
    Content
    Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.
    RSWK
    Datenanalyse / Kongress / Passau <2000>
    Automatische Klassifikation / Kongress / Passau <2000>
    Data Mining / Kongress / Passau <2000>
    World Wide Web / Wissensorganisation / Kongress / Passau <2000>
    Subject
    Datenanalyse / Kongress / Passau <2000>
    Automatische Klassifikation / Kongress / Passau <2000>
    Data Mining / Kongress / Passau <2000>
    World Wide Web / Wissensorganisation / Kongress / Passau <2000>
  3. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.05 
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    Abstract
    The Wolverhampton Web Library (WWLib) is a WWW search engine that provides access to UK based information. The experimental version developed in 1995, was a success but highlighted the need for a much higher degree of automation. An interesting feature of the experimental WWLib was that it organised information according to DDC. Discusses the advantages of classification and describes the automatic classifier that is being developed in Java as part of the new, fully automated WWLib
    Date
    1. 8.1996 22:08:06
    Footnote
    Contribution to a special issue devoted to the Proceedings of the 7th International World Wide Web Conference, held 14-18 April 1998, Brisbane, Australia; vgl. auch: http://www7.scu.edu.au/programme/posters/1846/com1846.htm.
    Theme
    Klassifikationssysteme im Online-Retrieval
  4. Möller, G.: Automatic classification of the World Wide Web using Universal Decimal Classification (1999) 0.04 
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    Imprint
    Hinskey Hill : Learned Information
    Source
    Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
    Theme
    Klassifikationssysteme im Online-Retrieval
  5. Wätjen, H.-J.: GERHARD : Automatisches Sammeln, Klassifizieren und Indexieren von wissenschaftlich relevanten Informationsressourcen im deutschen World Wide Web (1998) 0.04 
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    Abstract
    Die intellektuelle Erschließung des Internet befindet sich in einer Krise. Yahoo und andere Dienste können mit dem Wachstum des Web nicht mithalten. GERHARD ist derzeit weltweit der einzige Such- und Navigationsdienst, der die mit einem Roboter gesammelten Internetressourcen mit computerlinguistischen und statistischen Verfahren auch automatisch vollständig klassifiziert. Weit über eine Million HTML-Dokumente von wissenschaftlich relevanten Servern in Deutschland können wie bei anderen Suchmaschinen in der Datenbank gesucht, aber auch über die Navigation in der dreisprachigen Universalen Dezimalklassifikation (ETH-Bibliothek Zürich) recherchiert werden
    Theme
    Klassifikationssysteme im Online-Retrieval
  6. Koch, T.: Experiments with automatic classification of WAIS databases and indexing of WWW : some results from the Nordic WAIS/WWW project (1994) 0.03 
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    Abstract
    The Nordic WAIS/WWW project sponsored by NORDINFO is a joint project between Lund University Library and the National Technological Library of Denmark. It aims to improve the existing networked information discovery and retrieval tools Wide Area Information System (WAIS) and World Wide Web (WWW), and to move towards unifying WWW and WAIS. Details current results focusing on the WAIS side of the project. Describes research into automatic indexing and classification of WAIS sources, development of an orientation tool for WAIS, and development of a WAIS index of WWW resources
  7. Miyamoto, S.: Information clustering based an fuzzy multisets (2003) 0.03 
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    Abstract
    A fuzzy multiset model for information clustering is proposed with application to information retrieval on the World Wide Web. Noting that a search engine retrieves multiple occurrences of the same subjects with possibly different degrees of relevance, we observe that fuzzy multisets provide an appropriate model of information retrieval on the WWW. Information clustering which means both term clustering and document clustering is considered. Three methods of the hard c-means, fuzzy c-means, and an agglomerative method using cluster centers are proposed. Two distances between fuzzy multisets and algorithms for calculating cluster centers are defined. Theoretical properties concerning the clustering algorithms are studied. Illustrative examples are given to show how the algorithms work.
    Source
    Information processing and management. 39(2003) no.2, S.195-213
  8. Wätjen, H.-J.: Automatisches Sammeln, Klassifizieren und Indexieren von wissenschaftlich relevanten Informationsressourcen im deutschen World Wide Web : das DFG-Projekt GERHARD (1998) 0.03 
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    Theme
    Klassifikationssysteme im Online-Retrieval
  9. Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.03 
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    Abstract
    Die Menge der zu klassifizierenden Veröffentlichungen steigt spätestens seit der Existenz des World Wide Web schneller an, als sie intellektuell sachlich erschlossen werden kann. Daher werden Verfahren gesucht, um die Klassifizierung von Textobjekten zu automatisieren oder die intellektuelle Klassifizierung zumindest zu unterstützen. Seit 1968 gibt es Verfahren zur automatischen Dokumentenklassifizierung (Information Retrieval, kurz: IR) und seit 1992 zur automatischen Textklassifizierung (ATC: Automated Text Categorization). Seit immer mehr digitale Objekte im World Wide Web zur Verfügung stehen, haben Arbeiten zur automatischen Textklassifizierung seit ca. 1998 verstärkt zugenommen. Dazu gehören seit 1996 auch Arbeiten zur automatischen DDC-Klassifizierung bzw. RVK-Klassifizierung von bibliografischen Titeldatensätzen und Volltextdokumenten. Bei den Entwicklungen handelt es sich unseres Wissens bislang um experimentelle und keine im ständigen Betrieb befindlichen Systeme. Auch das VZG-Projekt Colibri/DDC ist seit 2006 u. a. mit der automatischen DDC-Klassifizierung befasst. Die diesbezüglichen Untersuchungen und Entwicklungen dienen zur Beantwortung der Forschungsfrage: "Ist es möglich, eine inhaltlich stimmige DDC-Titelklassifikation aller GVK-PLUS-Titeldatensätze automatisch zu erzielen?"
    Date
    22. 1.2010 14:41:24
  10. Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.02 
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    Abstract
    This study seeks to find out how human beings cluster Web pages naturally. Twenty Web pages retrieved by the Northem Light search engine for each of 10 queries were sorted by 3 subjects into categories that were natural or meaningful to them. lt was found that different subjects clustered the same set of Web pages quite differently and created different categories. The average inter-subject similarity of the clusters created was a low 0.27. Subjects created an average of 5.4 clusters for each sorting. The categories constructed can be divided into 10 types. About 1/3 of the categories created were topical. Another 20% of the categories relate to the degree of relevance or usefulness. The rest of the categories were subject-independent categories such as format, purpose, authoritativeness and direction to other sources. The authors plan to develop automatic methods for categorizing Web pages using the common categories created by the subjects. lt is hoped that the techniques developed can be used by Web search engines to automatically organize Web pages retrieved into categories that are natural to users. 1. Introduction The World Wide Web is an increasingly important source of information for people globally because of its ease of access, the ease of publishing, its ability to transcend geographic and national boundaries, its flexibility and heterogeneity and its dynamic nature. However, Web users also find it increasingly difficult to locate relevant and useful information in this vast information storehouse. Web search engines, despite their scope and power, appear to be quite ineffective. They retrieve too many pages, and though they attempt to rank retrieved pages in order of probable relevance, often the relevant documents do not appear in the top-ranked 10 or 20 documents displayed. Several studies have found that users do not know how to use the advanced features of Web search engines, and do not know how to formulate and re-formulate queries. Users also typically exert minimal effort in performing, evaluating and refining their searches, and are unwilling to scan more than 10 or 20 items retrieved (Jansen, Spink, Bateman & Saracevic, 1998). This suggests that the conventional ranked-list display of search results does not satisfy user requirements, and that better ways of presenting and summarizing search results have to be developed. One promising approach is to group retrieved pages into clusters or categories to allow users to navigate immediately to the "promising" clusters where the most useful Web pages are likely to be located. This approach has been adopted by a number of search engines (notably Northem Light) and search agents.
    Date
    12. 9.2004 9:56:22
  11. Yao, H.; Etzkorn, L.H.; Virani, S.: Automated classification and retrieval of reusable software components (2008) 0.02 
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    Abstract
    The authors describe their research which improves software reuse by using an automated approach to semantically search for and retrieve reusable software components in large software component repositories and on the World Wide Web (WWW). Using automation and smart (semantic) techniques, their approach speeds up the search and retrieval of reusable software components, while retaining good accuracy, and therefore improves the affordability of software reuse. A program understanding of software components and natural language understanding of user queries was employed. Then the software component descriptions were compared by matching the resulting semantic representations of the user queries to the semantic representations of the software components to search for software components that best match the user queries. A proof of concept system was developed to test the authors' approach. The results of this proof of concept system were compared to human experts, and statistical analysis was performed on the collected experimental data. The results from these experiments demonstrate that this automated semantic-based approach for software reusable component classification and retrieval is successful when compared to the labor-intensive results from the experts, thus showing that this approach can significantly benefit software reuse classification and retrieval.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.4, S.613-627
  12. Kwon, O.W.; Lee, J.H.: Text categorization based on k-nearest neighbor approach for web site classification (2003) 0.02 
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    Abstract
    Automatic categorization is a viable method to deal with the scaling problem on the World Wide Web. For Web site classification, this paper proposes the use of Web pages linked with the home page in a different manner from the sole use of home pages in previous research. To implement our proposed method, we derive a scheme for Web site classification based on the k-nearest neighbor (k-NN) approach. It consists of three phases: Web page selection (connectivity analysis), Web page classification, and Web site classification. Given a Web site, the Web page selection chooses several representative Web pages using connectivity analysis. The k-NN classifier next classifies each of the selected Web pages. Finally, the classified Web pages are extended to a classification of the entire Web site. To improve performance, we supplement the k-NN approach with a feature selection method and a term weighting scheme using markup tags, and also reform its document-document similarity measure. In our experiments on a Korean commercial Web directory, the proposed system, using both a home page and its linked pages, improved the performance of micro-averaging breakeven point by 30.02%, compared with an ordinary classification which uses a home page only.
    Source
    Information processing and management. 39(2003) no.1, S.25-44
  13. Search Engines and Beyond : Developing efficient knowledge management systems, April 19-20 1999, Boston, Mass (1999) 0.02 
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    Content
    Ramana Rao (Inxight, Palo Alto, CA) 7 ± 2 Insights on achieving Effective Information Access Session One: Updates and a twelve month perspective Danny Sullivan (Search Engine Watch, US / England) Portalization and other search trends Carol Tenopir (University of Tennessee) Search realities faced by end users and professional searchers Session Two: Today's search engines and beyond Daniel Hoogterp (Retrieval Technologies, McLean, VA) Effective presentation and utilization of search techniques Rick Kenny (Fulcrum Technologies, Ontario, Canada) Beyond document clustering: The knowledge impact statement Gary Stock (Ingenius, Kalamazoo, MI) Automated change monitoring Gary Culliss (Direct Hit, Wellesley Hills, MA) User popularity ranked search engines Byron Dom (IBM, CA) Automatically finding the best pages on the World Wide Web (CLEVER) Peter Tomassi (LookSmart, San Francisco, CA) Adding human intellect to search technology Session Three: Panel discussion: Human v automated categorization and editing Ev Brenner (New York, NY)- Chairman James Callan (University of Massachusetts, MA) Marc Krellenstein (Northern Light Technology, Cambridge, MA) Dan Miller (Ask Jeeves, Berkeley, CA) Session Four: Updates and a twelve month perspective Steve Arnold (AIT, Harrods Creek, KY) Review: The leading edge in search and retrieval software Ellen Voorhees (NIST, Gaithersburg, MD) TREC update Session Five: Search engines now and beyond Intelligent Agents John Snyder (Muscat, Cambridge, England) Practical issues behind intelligent agents Text summarization Therese Firmin, (Dept of Defense, Ft George G. Meade, MD) The TIPSTER/SUMMAC evaluation of automatic text summarization systems Cross language searching Elizabeth Liddy (TextWise, Syracuse, NY) A conceptual interlingua approach to cross-language retrieval. Video search and retrieval Armon Amir (IBM, Almaden, CA) CueVideo: Modular system for automatic indexing and browsing of video/audio Speech recognition Michael Witbrock (Lycos, Waltham, MA) Retrieval of spoken documents Visualization James A. Wise (Integral Visuals, Richland, WA) Information visualization in the new millennium: Emerging science or passing fashion? Text mining David Evans (Claritech, Pittsburgh, PA) Text mining - towards decision support
  14. Krüger, C.: Evaluation des WWW-Suchdienstes GERHARD unter besonderer Beachtung automatischer Indexierung (1999) 0.02 
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    Abstract
    Die vorliegende Arbeit beinhaltet eine Beschreibung und Evaluation des WWW - Suchdienstes GERHARD (German Harvest Automated Retrieval and Directory). GERHARD ist ein Such- und Navigationssystem für das deutsche World Wide Web, weiches ausschließlich wissenschaftlich relevante Dokumente sammelt, und diese auf der Basis computerlinguistischer und statistischer Methoden automatisch mit Hilfe eines bibliothekarischen Klassifikationssystems klassifiziert. Mit dem DFG - Projekt GERHARD ist der Versuch unternommen worden, mit einem auf einem automatischen Klassifizierungsverfahren basierenden World Wide Web - Dienst eine Alternative zu herkömmlichen Methoden der Interneterschließung zu entwickeln. GERHARD ist im deutschsprachigen Raum das einzige Verzeichnis von Internetressourcen, dessen Erstellung und Aktualisierung vollständig automatisch (also maschinell) erfolgt. GERHARD beschränkt sich dabei auf den Nachweis von Dokumenten auf wissenschaftlichen WWW - Servern. Die Grundidee dabei war, kostenintensive intellektuelle Erschließung und Klassifizierung von lnternetseiten durch computerlinguistische und statistische Methoden zu ersetzen, um auf diese Weise die nachgewiesenen Internetressourcen automatisch auf das Vokabular eines bibliothekarischen Klassifikationssystems abzubilden. GERHARD steht für German Harvest Automated Retrieval and Directory. Die WWW - Adresse (URL) von GERHARD lautet: http://www.gerhard.de. Im Rahmen der vorliegenden Diplomarbeit soll eine Beschreibung des Dienstes mit besonderem Schwerpunkt auf dem zugrundeliegenden Indexierungs- bzw. Klassifizierungssystem erfolgen und anschließend mit Hilfe eines kleinen Retrievaltests die Effektivität von GERHARD überprüft werden.
  15. Calado, P.; Cristo, M.; Gonçalves, M.A.; Moura, E.S. de; Ribeiro-Neto, B.; Ziviani, N.: Link-based similarity measures for the classification of Web documents (2006) 0.02 
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    Abstract
    Traditional text-based document classifiers tend to perform poorly an the Web. Text in Web documents is usually noisy and often does not contain enough information to determine their topic. However, the Web provides a different source that can be useful to document classification: its hyperlink structure. In this work, the authors evaluate how the link structure of the Web can be used to determine a measure of similarity appropriate for document classification. They experiment with five different similarity measures and determine their adequacy for predicting the topic of a Web page. Tests performed an a Web directory Show that link information alone allows classifying documents with an average precision of 86%. Further, when combined with a traditional textbased classifier, precision increases to values of up to 90%, representing gains that range from 63 to 132% over the use of text-based classification alone. Because the measures proposed in this article are straightforward to compute, they provide a practical and effective solution for Web classification and related information retrieval tasks. Further, the authors provide an important set of guidelines an how link structure can be used effectively to classify Web documents.
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.2, S.208-221
  16. Shen, D.; Chen, Z.; Yang, Q.; Zeng, H.J.; Zhang, B.; Lu, Y.; Ma, W.Y.: Web page classification through summarization (2004) 0.02 
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    Source
    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
  17. Dolin, R.; Agrawal, D.; El Abbadi, A.; Pearlman, J.: Using automated classification for summarizing and selecting heterogeneous information sources (1998) 0.02 
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    Abstract
    Information retrieval over the Internet increasingly requires the filtering of thousands of heterogeneous information sources. Important sources of information include not only traditional databases with structured data and queries, but also increasing numbers of non-traditional, semi- or unstructured collections such as Web sites, FTP archives, etc. As the number and variability of sources increases, new ways of automatically summarizing, discovering, and selecting collections relevant to a user's query are needed. One such method involves the use of classification schemes, such as the Library of Congress Classification (LCC), within which a collection may be represented based on its content, irrespective of the structure of the actual data or documents. For such a system to be useful in a large-scale distributed environment, it must be easy to use for both collection managers and users. As a result, it must be possible to classify documents automatically within a classification scheme. Furthermore, there must be a straightforward and intuitive interface with which the user may use the scheme to assist in information retrieval (IR). Our work with the Alexandria Digital Library (ADL) Project focuses on geo-referenced information, whether text, maps, aerial photographs, or satellite images. As a result, we have emphasized techniques which work with both text and non-text, such as combined textual and graphical queries, multi-dimensional indexing, and IR methods which are not solely dependent on words or phrases. Part of this work involves locating relevant online sources of information. In particular, we have designed and are currently testing aspects of an architecture, Pharos, which we believe will scale up to 1.000.000 heterogeneous sources. Pharos accommodates heterogeneity in content and format, both among multiple sources as well as within a single source. That is, we consider sources to include Web sites, FTP archives, newsgroups, and full digital libraries; all of these systems can include a wide variety of content and multimedia data formats. Pharos is based on the use of hierarchical classification schemes. These include not only well-known 'subject' (or 'concept') based schemes such as the Dewey Decimal System and the LCC, but also, for example, geographic classifications, which might be constructed as layers of smaller and smaller hierarchical longitude/latitude boxes. Pharos is designed to work with sophisticated queries which utilize subjects, geographical locations, temporal specifications, and other types of information domains. The Pharos architecture requires that hierarchically structured collection metadata be extracted so that it can be partitioned in such a way as to greatly enhance scalability. Automated classification is important to Pharos because it allows information sources to extract the requisite collection metadata automatically that must be distributed.
  18. Golub, K.: Automated subject classification of textual documents in the context of Web-based hierarchical browsing (2011) 0.02 
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    Abstract
    While automated methods for information organization have been around for several decades now, exponential growth of the World Wide Web has put them into the forefront of research in different communities, within which several approaches can be identified: 1) machine learning (algorithms that allow computers to improve their performance based on learning from pre-existing data); 2) document clustering (algorithms for unsupervised document organization and automated topic extraction); and 3) string matching (algorithms that match given strings within larger text). Here the aim was to automatically organize textual documents into hierarchical structures for subject browsing. The string-matching approach was tested using a controlled vocabulary (containing pre-selected and pre-defined authorized terms, each corresponding to only one concept). The results imply that an appropriate controlled vocabulary, with a sufficient number of entry terms designating classes, could in itself be a solution for automated classification. Then, if the same controlled vocabulary had an appropriat hierarchical structure, it would at the same time provide a good browsing structure for the collection of automatically classified documents.
  19. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.02 
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    Abstract
    This paper introduces a project to develop a reliable, cost-effective method for classifying Internet texts into register categories, and apply that approach to the analysis of a large corpus of web documents. To date, the project has proceeded in 2 key phases. First, we developed a bottom-up method for web register classification, asking end users of the web to utilize a decision-tree survey to code relevant situational characteristics of web documents, resulting in a bottom-up identification of register and subregister categories. We present details regarding the development and testing of this method through a series of 10 pilot studies. Then, in the second phase of our project we applied this procedure to a corpus of 53,000 web documents. An analysis of the results demonstrates the effectiveness of these methods for web register classification and provides a preliminary description of the types and distribution of registers on the web.
    Date
    4. 8.2015 19:22:04
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1817-1831
  20. Groß, T.; Faden, M.: Automatische Indexierung elektronischer Dokumente an der Deutschen Zentralbibliothek für Wirtschaftswissenschaften : Bericht über die Jahrestagung der Internationalen Buchwissenschaftlichen Gesellschaft (2010) 0.01 
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    Abstract
    Die zunehmende Verfügbarmachung digitaler Informationen in den letzten Jahren sowie die Aussicht auf ein weiteres Ansteigen der sogenannten Datenflut kumulieren in einem grundlegenden, sich weiter verstärkenden Informationsstrukturierungsproblem. Die stetige Zunahme von digitalen Informationsressourcen im World Wide Web sichert zwar jederzeit und ortsungebunden den Zugriff auf verschiedene Informationen; offen bleibt der strukturierte Zugang, insbesondere zu wissenschaftlichen Ressourcen. Angesichts der steigenden Anzahl elektronischer Inhalte und vor dem Hintergrund stagnierender bzw. knapper werdender personeller Ressourcen in der Sacherschließun schafft keine Bibliothek bzw. kein Bibliotheksverbund es mehr, weder aktuell noch zukünftig, alle digitalen Daten zu erfassen, zu strukturieren und zueinander in Beziehung zu setzen. In der Informationsgesellschaft des 21. Jahrhunderts wird es aber zunehmend wichtiger, die in der Flut verschwundenen wissenschaftlichen Informationen zeitnah, angemessen und vollständig zu strukturieren und somit als Basis für eine Wissensgenerierung wieder nutzbar zu machen. Eine normierte Inhaltserschließung digitaler Informationsressourcen ist deshalb für die Deutsche Zentralbibliothek für Wirtschaftswissenschaften (ZBW) als wichtige Informationsinfrastruktureinrichtung in diesem Bereich ein entscheidender und auch erfolgskritischer Aspekt im Wettbewerb mit anderen Informationsdienstleistern. Weil die traditionelle intellektuelle Sacherschließung aber nicht beliebig skalierbar ist - mit dem Anstieg der Zahl an Online-Dokumenten steigt proportional auch der personelle Ressourcenbedarf an Fachreferenten, wenn ein gewisser Qualitätsstandard gehalten werden soll - bedarf es zukünftig anderer Sacherschließungsverfahren. Automatisierte Verschlagwortungsmethoden werden dabei als einzige Möglichkeit angesehen, die bibliothekarische Sacherschließung auch im digitalen Zeitalter zukunftsfest auszugestalten. Zudem können maschinelle Ansätze dazu beitragen, die Heterogenitäten (Indexierungsinkonsistenzen) zwischen den einzelnen Sacherschließer zu nivellieren, und somit zu einer homogeneren Erschließung des Bibliotheksbestandes beitragen.

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