Computing interdisciplinarity of scholarly objects using an author-citation-text model

Abstract

There has been a growing need to determine if research proposals and results are truly interdisciplinary or to analyze research trends by analyzing research papers, reports, proposals and even researchers. In this paper, we tackle the problem and propose a method for measuring interdisciplinarity of scholarly objects. The newly proposed model takes into account authors, citations, and text content of scholarly objects together by building author networks, citation networks and text models. The three types of information are mixed by building network embeddings and sentence embeddings, which rely on the network topology and context-driven word semantics, respectively, through neural network learning. In addition, we propose a new measure that considers not only evenness of disciplines but also distributions of the magnitudes of disciplines so that saliency of disciplines is well represented.