Vineeth Venugopal

Massachusetts Institute of Technology

Postdoctoral Scholar


I am a Postdoctoral Scholar at the Massachusetts Institute of Technology working on knowledge/data extraction from scientific literature using Natural Language Processing. I am PhD in Materials Engineering from Brown University, USA where I studied piezoelectric materials and their fabrication. I am interested in the creation of AI/ML tools for materials discovery and development, as well as the generation of machine readable FAIR Data compliant datasets in the field.

Talks and Events

MatKG: The largest Knowledge Graph in Material Science

In the work, we present MatKG, the largest knowledge graph in the field of material science. It contains over 80,000 unique entities and over 5 million statements covering several topical fields such as inorganic oxides, functional materials, battery materials, metals and alloys, polymers, cements, high entropy alloys, biomaterials, and catalysts. The triples are generated autonomously through data driven natural language processing pipelines and extracted from a corpus of around 4 million published scientific articles. Several informational entities such as materials, properties, application areas, synthesis information, and characterization methods are integrated together with a hierarchical ontological schema, where the base relations are extracted through statistical correlations to which higher level ontologies are appended. We show that using a graph representation model we are able to perform link prediction allowing the correlation of materials with novel properties/application and vice versa.

Track: Ontologies, Taxonomies and Data Modeling

Session Topics:

  • Entity Resolution
  • Ontologies
  • Data Discoverability
  • Natural Language Processing and Understanding
  • Graph Data Science
  • Deep Learning for and with Knowledge Graphs
  • Graph Machine Learning
  • Content Knowledge Graphs
  • Material Science