Detecting interdisciplinary semantic drift for knowledge organization based on normal cloud model

•Propose an effective interdisciplinary concepts extraction method.•Employ the normal cloud model (NCM) for interdisciplinary semantic drift identification.•Conduct an empirical study on the interdisciplinary concept “information entropy”.•Visualize the semantic drift degree and direction of each interdisciplinary concept. To reduce the conceptual ambiguity in interdisciplinary knowledge organization systems (KOSs) and enhance interdisciplinary KOS management, this paper proposes a framework for interdisciplinary semantic drift (ISD) detection based on the normal cloud model (NCM). In this framework, we first analyze the features of interdisciplinary concepts and propose a novel interdisciplinary concept extraction method based on cross-discipline statistical information. Secondly, the high-performance knowledge representation model NCM is adopted to represent each interdisciplinary concept with uncertainty, and then a new ISD degree calculation method is proposed based on the similarity cloud algorithm. Thirdly, to identify the direction of ISD after the degree calculation, we propose an ISD direction identification method according to the theory of knowledge potential energy (KPE). Fourthly, based on the above procedure, we propose an ISD detection algorithm to identify and visualize the ISD process. Finally, we evaluate the proposed framework on the concept of “information entropy” and compare the performance with three baselines. Experimental results demonstrate that our framework outperforms[ all the baselines, and the result is comparable to experts’ judgments (0.808 on Spearman correlation, p