Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction
This work addresses the challenge of extracting structured data from materials science texts and tables for researchers, but it is incremental as it primarily identifies and quantifies existing problems without proposing new solutions.
The paper tackles the problem of automated information extraction from materials science literature, which is hindered by diverse formats and inconsistent reporting styles, and documents these challenges to aid in building a large materials knowledge base.
The discovery of new materials has a documented history of propelling human progress for centuries and more. The behaviour of a material is a function of its composition, structure, and properties, which further depend on its processing and testing conditions. Recent developments in deep learning and natural language processing have enabled information extraction at scale from published literature such as peer-reviewed publications, books, and patents. However, this information is spread in multiple formats, such as tables, text, and images, and with little or no uniformity in reporting style giving rise to several machine learning challenges. Here, we discuss, quantify, and document these challenges in automated information extraction (IE) from materials science literature towards the creation of a large materials science knowledge base. Specifically, we focus on IE from text and tables and outline several challenges with examples. We hope the present work inspires researchers to address the challenges in a coherent fashion, providing a fillip to IE towards developing a materials knowledge base.