  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.
