KnowGL: Knowledge Generation and Linking from Text
Keywords:Machine Learning, Natural Language Processing, Information Extraction, Knowledge Representation
AbstractWe 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 https://huggingface.co/ibm/knowgl-large.
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. https://doi.org/10.1609/aaai.v37i13.27084