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KnowGL: Knowledge Generation and Linking from Text

2022-10-25 12:12:36
Gaetano Rossiello, Faisal Chowdhury, Nandana Mihindukulasooriya, Owen Cornec, Alfio Gliozzo

Abstract

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 this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2210.13952

PDF

https://arxiv.org/pdf/2210.13952.pdf


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