KnowGL: Knowledge Generation and Linking from Text

by   Gaetano Rossiello, et al.

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


page 1

page 2

page 3


DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph

In this work, we present a web application named DBLPLink, which perform...

From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer

Knowledge graph completion aims to address the problem of extending a KG...

Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning

In this work, we aim at equipping pre-trained language models with struc...

Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models

We present a novel method for mapping unrestricted text to knowledge gra...

Multilingual Fact Linking

Knowledge-intensive NLP tasks can benefit from linking natural language ...

Conditional set generation using Seq2seq models

Conditional set generation learns a mapping from an input sequence of to...

Bootstrapping Text Anonymization Models with Distant Supervision

We propose a novel method to bootstrap text anonymization models based o...

Please sign up or login with your details

Forgot password? Click here to reset