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--- |
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language: |
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- en |
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widget: |
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- 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." |
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tags: |
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- seq2seq |
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- relation-extraction |
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- triple-generation |
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- entity-linking |
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- entity-type-linking |
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- relation-linking |
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model-index: |
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- name: knowgl |
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results: |
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- task: |
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name: Relation Extraction |
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type: Relation-Extraction |
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dataset: |
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name: "Babelscape/rebel-dataset" |
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type: REBEL |
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metrics: |
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- name: RE+ Macro F1 |
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type: re+ macro f1 |
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value: 70.74 |
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license: cc-by-nc-sa-4.0 |
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--- |
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# KnowGL: Knowledge Generation and Linking from Text |
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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 - |
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``` |
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[(subject mention # subject label # subject type) | relation label | (object mention # object label # object type)] |
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``` |
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If there is more than one triple generated, they are separated by `$` in the output. |
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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). |
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The generated labels (for the subject, relation, and object) and their types can be directly mapped to Wikidata IDs associated with them. |
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#### Citation |
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```bibtex |
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@article{DBLP:journals/corr/abs-2207-05188, |
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author = {Nandana Mihindukulasooriya and |
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Mike Sava and |
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Gaetano Rossiello and |
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Md. Faisal Mahbub Chowdhury and |
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Irene Yachbes and |
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Aditya Gidh and |
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Jillian Duckwitz and |
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Kovit Nisar and |
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Michael Santos and |
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Alfio Gliozzo}, |
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title = {Knowledge Graph Induction enabling Recommending and Trend Analysis: |
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{A} Corporate Research Community Use Case}, |
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journal = {CoRR}, |
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volume = {abs/2207.05188}, |
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year = {2022} |
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} |
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``` |
