Creating research topic map for NIMS SAMURAI database using natural language processing approach

In this study, we present an approach to create a visual research topic map for materials science researchers from a large collection of archived research papers using natural language processing (NLP). We apply this approach on SAMURAI, a directory service for the researchers of the National Institute for Materials Science (NIMS), Japan. Visualization of research content will support exploratory searches by maximizing the information absorbance and intuitively capturing the research characteristics of each materials science researcher. In addition, a research topic map can connect researchers with similar topics aiming to find potential collaborators. Collaboration can support the advance of scientific research. We analyze all available publications of a researcher using frequent term analysis. In addition, materials science knowledge resources were utilized including dictionaries and automatic extraction for the material names. Noise reduction was implemented using stop word filtering. The topics were then visualized using a word cloud technique for each researcher. An analysis of the topic similarity was conducted to find researchers that share similar topics leading to the creation of a topic map for each researcher. The approach aims at maximizing information absorbance for public knowledge by applying NLP approaches to information mining from materials science research papers. NLP analysis and visualization code are available https://github.com/ThaerDieb/Topic_map.