KnowGL: Knowledge Generation and Linking from Text


  • Gaetano Rossiello IBM Research AI
  • Md. Faisal Mahbub Chowdhury IBM Research AI
  • Nandana Mihindukulasooriya IBM Research AI
  • Owen Cornec IBM Research AI
  • Alfio Massimiliano Gliozzo IBM Research AI



Machine Learning, Natural Language Processing, Information Extraction, Knowledge Representation


We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG), such as Wikidata. We address this problem as a sequence generation task by leveraging pre-trained sequence-to-sequence language models, e.g. BART. Given a sentence, we fine-tune such models to detect pairs of entity mentions and jointly generate a set of facts consisting of the full set of semantic annotations for a KG, such as entity labels, entity types, and their relationships. To showcase the capabilities of our tool, we build a web application consisting of a set of UI widgets that help users to navigate through the semantic data extracted from a given input text. We make the KnowGL model available at




How to Cite

Rossiello, G., Chowdhury, M. F. M., Mihindukulasooriya, N., Cornec, O., & Gliozzo, A. M. (2023). KnowGL: Knowledge Generation and Linking from Text. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16476-16478.