A search strategy for publications in interdisciplinary research

Abstract

To retrieve the right collection of publications in interdisciplinary research, we have developed a search strategy with four progressive steps and take the area of public affairs (PA) as a case study. A set of seed publications in PA is first identified, followed by the construction of a pool set of publications with wider coverage for refinement in the next step, which is critical and in which an expanded set of publications is established on the basis of the references and text semantic information, thus generating two respective subsets. One of these subsets is obtained on the basis of the number of references shared between each publication pair between the seed set and the pool set. To optimize the results, we construct two models, viz. a support vector machine (SVM) and a fully connected neural network (FCNN), and find that the FCNN model outperforms the SVM model. The second subset of publications are collected by selecting the publications with high topic similarity to the seed publications collected in the first step. The final step is to integrate the seed publications with the expanded publications collected in steps 1 and 3. The results show that PA research involves an extremely wide range of disciplines (n = 45), among which public administration, environmental sciences, economics, management, and health policy and services, among others, play the most significant roles.