Looking through glass: Knowledge discovery from materials science literature using natural language processing

Vineeth Venugopal; Sourav Sahoo; Mohd Zaki; Manish Agarwal; Nitya Nand Gosvami; N M Anoop Krishnan

doi:10.1016/j.patter.2021.100290

Looking through glass: Knowledge discovery from materials science literature using natural language processing

Patterns (N Y). 2021 Jun 24;2(7):100290. doi: 10.1016/j.patter.2021.100290. eCollection 2021 Jul 9.

Authors

Vineeth Venugopal¹, Sourav Sahoo², Mohd Zaki¹, Manish Agarwal³, Nitya Nand Gosvami², N M Anoop Krishnan^{1

2}

Affiliations

¹ Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
² Department of Materials Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
³ Computer Services Center, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.

Abstract

Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision knowledge extraction from inorganic glasses' literature. The abstracts are automatically categorized using latent Dirichlet allocation (LDA) to classify and search semantically linked publications. Similarly, a comprehensive summary of images and plots is presented using the caption cluster plot (CCP), providing direct access to images buried in the papers. Finally, we combine the LDA and CCP with chemical elements to present an elemental map, a topical and image-wise distribution of elements occurring in the literature. Overall, the framework presented here can be a generic and powerful tool to extract and disseminate material-specific information on composition-structure-processing-property dataspaces, allowing insights into fundamental problems relevant to the materials science community and accelerated materials discovery.

Keywords: artificial intelligence; glass science; knowledge discovery; materials science; natural language processing.