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: 04. Juni 2021)
1Chen, H. ; Zhang, Y. ; Houston, A.L.: Semantic indexing and searching using a Hopfield net.
In: Journal of information science. 24(1998) no.1, S.3-18.
Abstract: Presents a neural network approach to document semantic indexing. Reports results of a study to apply a Hopfield net algorithm to simulate human associative memory for concept exploration in the domain of computer science and engineering. The INSPEC database, consisting of 320.000 abstracts from leading periodical articles was used as the document test bed. Benchmark tests conformed that 3 parameters: maximum number of activated nodes; maximum allowable error; and maximum number of iterations; were useful in positively influencing network convergence behaviour without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests conformed expectations that the Hopfield net is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end user vocabularies
Themenfeld: Retrievalalgorithmen ; Semantisches Umfeld in Indexierung u. Retrieval
2Orwig, R.E. ; Chen, H. ; Nunamaker, J.F.: ¬A graphical, self-organizing approach to classifying electronic meeting output.
In: Journal of the American Society for Information Science. 48(1997) no.2, S.157-170.
Abstract: Describes research in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. Describes an electronic meeting system and describes the classification problem that exists in the group problem solving process. Surveys the literature concerning classification. Describes the application of the Kohonen SOM to the meeting output classification problem. Describes an experiment that evaluated the classification performed by the Kohonen SOM by comparing it with those of a human expert and a Hopfield neural network. Discusses conclusions and directions for future research
Themenfeld: Automatisches Klassifizieren
Objekt: Hopfield-Netze ; Kohonen-Netz ; SOM
3Chen, H. ; Ng, T.: ¬An algorithmic approach to concept exploration in a large knowledge network (automatic thesaurus consultation) : symbolic branch-and-bound search versus connectionist Hopfield Net Activation.
In: Journal of the American Society for Information Science. 46(1995) no.5, S.348-369.
Abstract: Presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge based systems and to alleviate the limitation of the manual browsing approach, develops 2 spreading activation based algorithms for concept exploration in large, heterogeneous networks of concepts (eg multiple thesauri). One algorithm, which is based on the symbolic AI paradigma, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The 2nd algorithm, which is absed on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify 'convergent' concepts for some initial queries (a parallel, heuristic search process). Tests these 2 algorithms on a large text-based knowledge network of about 13.000 nodes (terms) and 80.000 directed links in the area of computing technologies
Themenfeld: Konzeption und Anwendung des Prinzips Thesaurus ; Inhaltsanalyse
4Chen, H.: Machine learning for information retrieval : neural networks, symbolic learning, and genetic algorithms.
In: Journal of the American Society for Information Science. 46(1995) no.3, S.194-216.
Abstract: In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, researchers have turned to newer artificial intelligence based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms grounded on diverse paradigms. These have provided great opportunities to enhance the capabilities of current information storage and retrieval systems. Provides an overview of these techniques and presents 3 popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evaluation based genetic algorithms in the context of information retrieval. The techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search and can greatly complement the prevailing full text, keyword based, probabilistic, and knowledge based techniques