The Largest Knowledge Graph in Materials Science - Entities, Relations, and Link Prediction through Graph Representation Learning

Vineeth Venugopal, Sumit Pai, Elsa Olivetti

Keywords: Material Science, Materials Informatics, Knowledge Graph, NLP, Natural Language Processing, AI

TL;DR: Autonomously developed knowledge graph for material science enables the visualization, curation, and discovery of knowledge.

Abstract: This paper introduces MatKG, a novel graph database of key concepts in material science spanning the traditional material-structure-property-processing paradigm. MatKG is autonomously generated through transformer-based, large language models and generates pseudo ontological schema through statistical co-occurrence mapping. At present, MatKG contains over 2 million unique relationship triples derived from 80,000 entities. This allows the curated analysis, querying, and visualization of materials knowledge at unique resolution and scale. Further, Knowledge Graph Embedding models are used to learn embedding representations of nodes in the graph which are used for downstream tasks such as link prediction and entity disambiguation. MatKG allows the rapid dissemination and assimilation of data when used as a knowledge base, while enabling the discovery of new relations when trained as an embedding model.

Paper Track: Papers

Submission Category: AI-Guided Design, Automated Chemical Synthesis, Automated Material Characterization