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 procedures 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 proposed 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 frequency, 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 scientosemantic domains ranges from about 30 to 800. Since indicator-based comparison of scientosemantic 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 developmentbased 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 investment appeal in technology-related keywords.

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