Knowledge Discovery Through Co-Word Analysis QINHE

IN THE LAST HALF CENTURY, AS THE SCIENCE I,ITERATURE has increased dramatically, scientists found it increasingly difficult to locate needed data, and it is increasingly difficult for policymakers to understand the complex interrelationship of science in order to achieve effective research planning. Some quantitative techniques have been developed to ameliorate these problems; co-word analysis is one of these techniques. Based on the co-occurrence frequency of pairs of words or phrases, co-word analysis is used to discover linkages among subjects in a research field and thus to trace the development of science. Within the last two decades, this technique, implemented by several research groups, has proved to be a powerful tool for knowledge discovery in databases. This article reviews the development of co-word analysis, summarizes the advantages and disadvantages of this method, and discusses several research issues. INTRODUCTION Since World War 11, the scope and volume of scientific research have increased dramatically. This is well reflected in the growth of the literature. In the 1960s, the amount of scientific literature was estimated to be doubling approximately every ten years (Price, 1963). Three decades later, in the 199Os, along with developments in information technolo<y, especially in the area of data storage, the amount of information in the world is estimated to be doubling every twenty months (Frdwley et al., 1991). In such a situation, it is hard for scientists to detect the subject areas and the Qin He, Graduate School of Library and Information Science, University of Illinois, 501 E. Daniel Street, Champaign, IL 61820 LIBRARY TRENDS, Vol. 48, No. 1,Summer 1999,pp. 133-159 01999 The Board of Trustees, University of Illinois 134 LIBRARY TRENDS/SUMMER 1999 linkages among these areas in their research fields, and policy makers have difficulties in mapping the dynamics of science to do research planning. The traditional way to map the relationships among concepts, ideas, and problems in science is to seek the views of a relatively small number of experts. Even though such methods are indispensable for some purposes, as Law and Whittaker (1992) said, they also have certain drawbacks: First, they are extremely expensive unless the survey of experts is very small. Second, if the survey is small, then its representativeness is open to question. Third, the problem of collating a range of views about the way in which science has developed or is developing is complex. (pp. 417-418) For these reasons, quantitative methods for mapping the structure of science have been developed; they include co-citation analysis, co-nomination analysis, and co-word analysis. This article reviews the development of the co-word analysis technique. Co-word analysis is a content analysis technique that uses patterns of co-occurrence of pairs of items (i.e., words or noun phrases) in a corpus of texts to identify the relationships between ideas within the subject areas presented in these texts. Indexes based on the co-occurrence frequency of items, such as an inclusion index and a proximity index, are used to measure the strength of relationships between items. Based on these indexes, items are clustered into groups and displayed in network maps. For example, an inclusion map is used to highlight the central themes in a domain, and a proximity map is used to reveal the connections between minor areas hidden behind the central ones. Some other indexes, such as those based on density and centrality, are employed to evaluate the shape of each map, which shows the degree to which each area is centrally structured and the extent to which each area is central to the others. By comparing the network maps for different time periods, the dynamic of science can be detected. The co-word analysis technique was first developed in collaboration between the Centre de Sociologie de 1’Innovation of the Ecole Nationale Superieure des Mines of Paris and the CNRS (Centre National de la Recherche Scientifique) of France during the 198Os, and their system was called “LEXIMAPPE.” For about twenty years, this technique has been employed to map the dynamic development of several research fields. One of the early studies was carried out by Serge Bauin (1986) to map the dynamics of aquaculture from 1979 to 1981. Based on the inclusion and proximity indexes, inclusion and proximity maps were created for 19’19 and 1981. With the decomposition of keywords into central poles and mediator words, the inclusion map for 1979 is shown in Figure 1and that for 1981 is shown in Figure 2. HE/KD THROUGH CO-WORD ANALYSIS 135 aquaculture development freshwater fishery aquaculture development