---
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 [Rossiello et al. (AAAI 2023)](https://arxiv.org/pdf/2210.13952.pdf) 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{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},
  journal   = {Proceedings of the AAAI Conference on Artificial Intelligence}
  year      = {2023}
}
```

```bibtex
@InProceedings{10.1007/978-3-031-19433-7_47,
  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 = "The Semantic Web -- ISWC 2022",
  year      = "2022",
  publisher = "Springer International Publishing",
  pages     = "827--844",
}
```