Literatur zur Informationserschließung

Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.

© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 28. April 2022)

Suchergebnisse

• 1
  Ozmutlu, H.C. ; Cavdur, F. ; Ozmutlu, S.: Cross-validation of neural network applications for automatic new topic identification.
  In: Journal of the American Society for Information Science and Technology. 59(2008) no.3, S.339-362.
  
  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.
  
  
  
• 2
  Ozumutlu, H.C. ; Cavdur, F.: ¬Application of automatic topic identification on Excite Web search engine data logs.
  In: Information processing and management. 41(2005) no.5, S.1243-1262.
  
  Abstract: The analysis of contextual information in search engine query logs enhances the understanding of Web users' search patterns. Obtaining contextual information on Web search engine logs is a difficult task, since users submit few number of queries, and search multiple topics. Identification of topic changes within a search session is an important branch of search engine user behavior analysis. The purpose of this study is to investigate the properties of a specific topic identification methodology in detail, and to test its validity. The topic identification algorithm's performance becomes doubtful in various cases. These cases are explored and the reasons underlying the inconsistent performance of automatic topic identification are investigated with statistical analysis and experimental design techniques.
  
  Themenfeld: Suchmaschinen
  Objekt: Excite