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--- |
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language: |
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- en |
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license: cc-by-nc-sa-4.0 |
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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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datasets: Babelscape/rebel-dataset |
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widget: |
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- text: The Italian Space Agency’s Light Italian CubeSat for Imaging of Asteroids, |
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or LICIACube, will fly by Dimorphos to capture images and video of the impact |
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plume as it sprays up off the asteroid and maybe even spy the crater it could |
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leave behind. |
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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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type: Relation-Extraction |
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name: 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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- type: re+ macro f1 |
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value: 70.74 |
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name: RE+ Macro F1 |
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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 in the [REBEL](https://huggingface.co/datasets/Babelscape/rebel-dataset) dataset. Given a sentence, KnowGL 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 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). |
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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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@inproceedings{DBLP:conf/aaai/RossielloCMCG23, |
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author = {Gaetano Rossiello and |
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Md. Faisal Mahbub Chowdhury and |
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Nandana Mihindukulasooriya and |
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Owen Cornec and |
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Alfio Massimiliano Gliozzo}, |
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title = {KnowGL: Knowledge Generation and Linking from Text}, |
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booktitle = {{AAAI}}, |
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pages = {16476--16478}, |
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publisher = {{AAAI} Press}, |
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year = {2023} |
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} |
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``` |
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```bibtex |
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@inproceedings{DBLP:conf/semweb/Mihindukulasooriya22, |
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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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booktitle = {{ISWC}}, |
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series = {Lecture Notes in Computer Science}, |
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volume = {13489}, |
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pages = {827--844}, |
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publisher = {Springer}, |
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year = {2022} |
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} |
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``` |
