Predicting research trends with semantic and neural networks with an application in quantum physics

Abstract

The vast and growing number of publications in all disciplines of science cannot be comprehended by a single human researcher. As a consequence, researchers have to specialize in narrow subdisciplines, which makes it challenging to uncover scientific connections beyond the own field of research. Thus access to structured knowledge from a large corpus of publications could help pushing the frontiers of science. Here we demonstrate a method to build a semantic network from published scientific literature, which we call SEMNET. We use SEMNET to predict future trends in research and to inspire new, personalized and surprising seeds of ideas in science. We apply it in the discipline of quantum physics, which has seen an unprecedented growth of activity in recent years. In SEMNET, scientific knowledge is represented as an evolving network using the content of 750,000 scientific papers published since 1919. The nodes of the network correspond to physical concepts, and links between two nodes are drawn when two physical concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside SEMNET thus confirm that it stores useful semantic knowledge. We train a deep neural network using states of SEMNET of the past, to predict future developments in quantum physics research, and confirm high quality predictions using historic data. With the neural network and theoretical network tools we are able to suggest new, personalized, out-of-the-box ideas, by identifying pairs of concepts which have unique and extremal semantic network properties. Finally, we consider possible future developments and implications of our findings.

Machine learning for quantum matter

“…[89] uses supervised learning with a deep convolutional neural network to identify the equation of state in the relativistic hydrodynamic simulations of heavy ion collisions, which is one important goal of high-energy heavyion experiments. Additionally, machine learning techniques have been applied to the discovery of physical concepts and effective models [90] from data, and to the identification of physical theories [91][92][93], the discovery of symmetry and conserved quantities [94], and even to generate computer-inspired scientific ideas [95].…”

Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

“…HCBASIC (Peganova et al, 2019) is an adaptation of ComboBasic, also taking the hierarchical structure of clusters into account. Rapid Automatic Keyword Extraction (RAKE) (Rose et al, 2010) is another key-phrase extraction method, which has been utilized successfully in numerous studies for similar purposes, such as a study by Krenn and Zeilinger (Krenn & Zeilinger, 2020), for "concept extraction''.…”

Knowledge Graph to determine the domain of learning in Higher Education

“…Hay gran cantidad de investigaciones que aplican el GC para representar el conocimiento (Bellomarini et al, 2020;Jia et al, 2017;Krenn & Zeilinger, 2020;Lin et al, 2017;Zhu et al, 2019). La estructura del GC facilita la utilización de ponderaciones que permiten obtener nuevo conocimiento y conclusiones de datos existentes Li & Madden, 2019;.…”