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  • × author_ss:"Berg, J. van den"
  • × theme_ss:"Informetrie"
  1. Eijk, C.C. van der; Mulligen, E.M. van; Kors, J.A.; Mons, B.; Berg, J. van den: Constructing an associative concept space for literature-based discovery (2004) 0.00 
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    Abstract
    Scientific literature is often fragmented, which implies that certain scientific questions can only be answered by combining information from various articles. In this paper, a new algorithm is proposed for finding associations between related concepts present in literature. To this end, concepts are mapped to a multidimensional space by a Hebbian type of learning algorithm using co-occurrence data as input. The resulting concept space allows exploration of the neighborhood of a concept and finding potentially novel relationships between concepts. The obtained information retrieval system is useful for finding literature supporting hypotheses and for discovering previously unknown relationships between concepts. Tests an artificial data show the potential of the proposed methodology. In addition, preliminary tests an a set of Medline abstracts yield promising results.
    Type
    a
  2. Eck, N.J. van; Waltman, L.; Dekker, R.; Berg, J. van den: ¬A comparison of two techniques for bibliometric mapping : multidimensional scaling and VOS (2010) 0.00 
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    Abstract
    VOS is a new mapping technique that can serve as an alternative to the well-known technique of multidimensional scaling (MDS). We present an extensive comparison between the use of MDS and the use of VOS for constructing bibliometric maps. In our theoretical analysis, we show the mathematical relation between the two techniques. In our empirical analysis, we use the techniques for constructing maps of authors, journals, and keywords. Two commonly used approaches to bibliometric mapping, both based on MDS, turn out to produce maps that suffer from artifacts. Maps constructed using VOS turn out not to have this problem. We conclude that in general maps constructed using VOS provide a more satisfactory representation of a dataset than maps constructed using well-known MDS approaches.
    Type
    a

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