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---
language:
- en
license: cc-by-nc-sa-4.0
tags:
- seq2seq
- relation-extraction
- triple-generation
- entity-linking
- entity-type-linking
- relation-linking
datasets: Babelscape/rebel-dataset
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.
model-index:
- name: knowgl
results:
- task:
type: Relation-Extraction
name: Relation Extraction
dataset:
name: Babelscape/rebel-dataset
type: REBEL
metrics:
- type: re+ micro f1
value: 70.74
name: RE+ Micro F1
---
# KnowGL: Knowledge Generation and Linking from Text
The `knowgl-large` model is trained by combining Wikidata with an extended version of the training data in the [REBEL](https://huggingface.co/datasets/Babelscape/rebel-dataset) dataset. Given a sentence, KnowGL generates triple(s) in the following format:
```
[(subject mention # subject label # subject type) | relation label | (object mention # object label # object type)]
```
If there are more than one triples generated, they are separated by `$` in the output. More details in [Rossiello et al. (AAAI 2023)](https://arxiv.org/pdf/2210.13952.pdf).
The model achieves good 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
@inproceedings{knowgl-aaai_2023_demo,
author = {Gaetano Rossiello and
Md. Faisal Mahbub Chowdhury and
Nandana Mihindukulasooriya and
Owen Cornec and
Alfio Gliozzo},
title = {KnowGL: Knowledge Generation and Linking from Text},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}
year = {2023}
}
```
```bibtex
@inproceedings{DBLP:conf/semweb/Mihindukulasooriya22,
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},
booktitle = {{ISWC}},
series = {Lecture Notes in Computer Science},
volume = {13489},
pages = {827--844},
publisher = {Springer},
year = {2022}
}
``` |