Semantic domain comparison of research keywords by indicator-based fuzzy distances: A new prospect

Assessing the similarity of scientific outputs based on an indicator has not been addressed much so far. The topic, however, may find several potential applications which can help enrich pro-cedures of ranking, research monitoring, and scientific policy-making. The present study offers a new method to quantify such similarities based on keyword co-occurrence matrices. In the pro-posed method, first, the keyword co-occurrence networks are transformed into their associated newly defined fuzzy sets, named as scientosemantic domains. Then, a fuzzy distance between the two domains is found based on an arbitrary indicator. In this paper, the three indicators of fre-quency, development and investment appeal are used. The proposed method is implemented for five types of concept comparison. For each type, concepts are represented by a canonical keyword with different field codes. Scientosemantic domains of concepts are sourced out of bibliometric data obtained from appropriate queries on SCOPUS. Number of keywords used to define scien-tosemantic domains ranges from about 30 to 800. Since indicator-based comparison of sciento-semantic domains are not dealt with in the literature, the obtained distances between concepts are verified by qualitative and expert evaluations. For all cases, frequency-and development-based distances are less than those for investment appeal; while crisp distances for the latter extend beyond 0.6, the former does not exceed 0.3. The greatest distances are observed for in-vestment appeal in technology-related keywords.

Automated summary

This study proposes a method to assess the similarity of scientific outputs based on keyword co-occurrence matrices, which can be applied to ranking, research monitoring, and scientific policy-making. The method involves transforming keyword co-occurrence networks into scientosemantic domains and calculating fuzzy distances between domains using frequency, development, and investment appeal indicators. The bibliometric data from appropriate queries on SCOPUS is used to derive scientosemantic domains. The results show that the distances based on investment appeal are greater than those based on frequency and development, with the largest distances observed for technology-related keywords.