Number of found records: 23
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HONKELA, Timo |
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Self-Organizing maps in natural LanguageProcessing |
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Thesis for the degree of Doctor of Philosophy. Helsinki University of Technology on Friday, 12th of December 1997 |
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On line ( 15/06/2004) |
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Kohonen's Self-Organizing Map (SOM) is one of the most popular artificial neural network algorithms. Word category maps are SOMs that have been organized according to word similarities, measured by the similarity of the short contexts of the words. Conceptually interrelated words tend to fall into the same or neighboring map nodes. Nodes may thus be viewed as word categories. Although no a priori information about classes is given, during the self-organizing process a model of the word classes emerges. The central topic of the thesis is the use of the SOM in natural language processing. The approach based on the word category maps is compared with the methods that are widely used in artificial intelligence research. Modeling gradience, conceptual change, and subjectivity of natural language interpretation are considered. The main application area is information retrieval and textual data mining for which a specific SOM-based method called the WEBSOM has been developed. The WEBSOM method organizes a document collection on a map display that provides an overview of the collection and facilitates interactive browsing. (AU) |
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neural network; similarity; natural language processing; information retrieval; data mining |
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Comisión Europea |
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The RISE Semantic Network |
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Commissione Europea - Fondo Social Europeo - D.G. V I.C. ADAPT II Fase (1997-1999). |
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On line ( 15/06/2004) |
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RISE is a project aimed at the creation of a new Knowledge Management System that provides services to the social actors (trade unions, firms, workers, managers, planners, training organisations, research bodies, etc.) in order to share and develop the knowledge about the best practices related to the development of the Information Society and to the introduction of Information and Communication Technologies (ICT). This Knowledge Management System, lied to Adapt B.I.S. (Building the Information Society) references, has to be able to support various goals and contents, depending on the needs of the actors: adaptation of the workforce to the organisational and technological changes, new industrial relations, new training, job design, (re)qualification, business process redesign, innovative use of the technologies, quality of the working life, etc. (Web) |
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semantic project; knowledge management; RISE |
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HSINCHUN, Chen |
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Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms |
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Journal of the American Society for Information Science, 1995, Vol. 46, n.3, pp. 194-216. |
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On line ( 15/06/2004) |
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Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s knowledge-based techniques also made an impressive contribution to ``intelligent'' information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems.
In this article we first provide an overview of these newer techniques and their use in information science research. In order to familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network, the symbolic ID3/ID5R, and evolution-based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are robust in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keyword-based, probabilistic, and knowledge-based techniques. (AU) |
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information retrieval; indexing; neural networks; artificial intelligence; knowledge representation |
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JAENECKE, Peter |
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On the Structure of a Global Knowledge Space |
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Congreso ISKO España, 2001 |
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On line (09/05/2005) |
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A semantic network as a formal basis for a knowledge global space is presented here. The basic components of this space are the knowledge modules. These structures are explained in terms of semantic networks terminology. Yet, these can also been considered semantically as a language marker of the topic map kind. Likewise, modules are described as basic units which make the construction of scientific theories. |
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knowledge representation; epistemology |
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