readme: ---
  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 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 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
  @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}
  }
  ```