A metadata-based approach for research discipline prediction using machine learning techniques and distance metrics

Abstract

Forecasting research disciplines associated with research projects is a significant challenge in research information systems. It can reduce the administrative effort involved in entering research project-related metadata, eliminate human errors, and enhance the quality of research projects metadata. It also enables the calculation of the degree of interdisciplinarity of these projects. However, predicting scientific research disciplines and measuring interdisciplinarity in a research endeavor remain difficult. In this paper, we propose a framework for predicting the research disciplines associated with a research project and measuring the degree of interdisciplinarity based on associated metadata to address these issues. The proposed framework consists of several components to improve the performance of research disciplines prediction and interdisciplinarity measurement systems. These include a feature extraction component that utilizes a topic model to extract the most appropriate features. Further, the framework proposes a discipline encoding component that applies a data mapping strategy to lower the dimensionality of the output variables. Furthermore, a distance matrix creation component is proposed to recommend the most appropriate research disciplines and compute interdisciplinarity associated with research projects. We implemented the suggested framework on two separate research information systems databases for research projects, Dimensions and the Flemish Research Information Space. Experimental results demonstrate that the proposed framework predicts the research disciplines associated with research projects more accurately than related work.