Search (7 results, page 1 of 1)

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Abstract

The classification of documents from a bibliographic database is a task that is linked to processes of information retrieval based on partial matching. A method is described of vectorizing reference documents from LISA which permits their topological organization using Kohonen's algorithm. As an example a map is generated of 202 documents from LISA, and an analysis is made of the possibilities of this type of neural network with respect to the development of information retrieval systems based on graphical browsing.

Source

Information processing and management. 38(2002) no.1, S.79-89

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Abstract

The representation of information contents by graphical maps is an extended ongoing research topic. In this paper we introduce the application of UDC codes for the subject maps development. We use the following graphic representation methodologies: 1) Multidimensional scaling (MDS), 2) Cluster analysis, 3) Neural networks (Self Organizing Map - SOM). Finally, we conclude about the application viability of every kind of map. 1. Introduction Advanced techniques for Information Retrieval (IR) currently make up one of the most active areas for research in the field of library and information science. New models representing document content are replacing the classic systems in which the search terms supplied by the user were compared against the indexing terms existing in the inverted files of a database. One of the topics most often studied in the last years is bibliographic browsing, a good complement to querying strategies. Since the 80's, many authors have treated this topic. For example, Ellis establishes that browsing is based an three different types of tasks: identification, familiarization and differentiation (Ellis, 1989). On the other hand, Cove indicates three different browsing types: searching browsing, general purpose browsing and serendipity browsing (Cove, 1988). Marcia Bates presents six different types (Bates, 1989), although the classification of Bawden is the one that really interests us: 1) similarity comparison, 2) structure driven, 3) global vision (Bawden, 1993). The global vision browsing implies the use of graphic representations, which we will call map displays, that allow the user to get a global idea of the nature and structure of the information in the database. In the 90's, several authors worked an this research line, developing different types of maps. One of the most active was Xia Lin what introduced the concept of Graphical Table of Contents (GTOC), comparing the maps to true table of contents based an graphic representations (Lin 1996). Lin applies the algorithm SOM to his own personal bibliography, analyzed in function of the words of the title and abstract fields, and represented in a two-dimensional map (Lin 1997). Later on, Lin applied this type of maps to create websites GTOCs, through a Java application.

Date

12. 9.2004 14:31:22

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Abstract

One of the key problem that non-expert users have to deal with when using an Information Retrieval System is the need to deeply know its query language in order to express their information needs in the form of a valid query allowing them to retrieve relevant information. To solve this problem, Inductive Query by Example techniques can be considered to automatically derive queries from a set of relevant documents provided by a user. In this paper, a new hybrid evolutionary technique is proposed to automatically leam extended Boolean queries and is compared to Kraft et al.'s approach in several queries of the well known Cranfield collection.

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Abstract

Knowledge changes and evolves with time. This makes it necessary for classifications and other information retrieval tools to be revised periodically to ensure that they represent an updated organization of knowledge. This problem worsens in the case of some scientific disciplines because of their degree of internal specialization and dynamism. This is the case with Biotechnology and Applied Microbiology (category taken from the Journal Citation Reports (JCR)). A way of solving this problem can be by means of an automatic classification, but also by applying multivariate techniques of analysis and neural networks. This work deals with the two last procedures. The source chosen for doing this work is the Science Citation Index (SCI-CD) database, chosen because it contains the best publications on the disciplines studied - a guarantee for them to be represented properly. We are especially concerned with the quality of the representation. All of the journals dealing with Biotechnology and Applied Microbiology in the database (152) have been selected. Each document has been represented according to the references that it includes. From this point three co-citation matrices were generated: authors, journals and articles from each document. This technique implies the use of complementary methods for document representation by the way of the three matrices processed by multivariate techniques (MDS, PCA, CA). The result is that it can be determined which classes form the intellectual structure of the disciplines studied. This would be of great help for automatic updating of bibliographic classifications and other information retrieval tools. At the same time, these techniques can work as robots for document classification, authors and publications, and allows for the generation of knowledge maps that can be used as interfaces for accessing the documents from their location in a database..

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Source

Journal of the American Society for Information Science and technology. 52(2001) no.14, S.1234-1241

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Source

Journal of the American Society for Information Science and Technology. 63(2012) no.3, S.481-489

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Source

Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2310-2316