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---
language:
- en
widget:
- text: "The Italian Space Agency’s Light Italian CubeSat for Imaging of Asteroids, or LICIACube, will fly by Dimorphos to capture images and video of the impact plume as it sprays up off the asteroid and maybe even spy the crater it could leave behind."
tags:
- seq2seq
- relation-extraction
- triple-generation
- entity-linking
- entity-type-linking
- relation-linking
model-index:
- name: knowgl
results:
- task:
name: Relation Extraction
type: Relation-Extraction
dataset:
name: "Babelscape/rebel-dataset"
type: REBEL
metrics:
- name: RE+ Macro F1
type: re+ macro f1
value: 70.74
license: cc-by-nc-sa-4.0
---
# KnowGL: Knowledge Generation and Linking from Text
The `knowgl-large` model is trained by combining Wikidata with an extended version of the training data [REBEL](https://huggingface.co/datasets/Babelscape/rebel-dataset) dataset. Given a sentence, it generates triple(s) in the following format -
```
[(subject mention # subject label # subject type) | relation label | (object mention # object label # object type)]
```
If there is more than one triple generated, they are separated by `$` in the output.
The model achieves state-of-the-art results for relation extraction on the REBEL dataset. See results in [Mihindukulasooriya et al (ISWC 2022)](https://arxiv.org/pdf/2207.05188.pdf).
The generated labels (for the subject, relation, and object) and their types can be directly mapped to Wikidata IDs associated with them.
#### Citation
```bibtex
@article{DBLP:journals/corr/abs-2207-05188,
author = {Nandana Mihindukulasooriya and
Mike Sava and
Gaetano Rossiello and
Md. Faisal Mahbub Chowdhury and
Irene Yachbes and
Aditya Gidh and
Jillian Duckwitz and
Kovit Nisar and
Michael Santos and
Alfio Gliozzo},
title = {Knowledge Graph Induction enabling Recommending and Trend Analysis:
{A} Corporate Research Community Use Case},
journal = {CoRR},
volume = {abs/2207.05188},
year = {2022}
}
``` |