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--- |
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license: cc-by-4.0 |
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--- |
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# Clean ConceptNet Data for All Languages |
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## Data Details |
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For our project on [Retrofitting Glove embeddings for Low Resource Languages](https://github.com/pyRis/retrofitting-embeddings-lrls/tree/main?tab=readme-ov-file), we extracted all data from the [ConceptNet](https://github.com/commonsense/conceptnet5/wiki/Downloads) database for 304 languages. The extraction process involved several steps to clean and analyze the data from the official ConceptNet dump available [here](https://s3.amazonaws.com/conceptnet/downloads/2019/edges/conceptnet-assertions-5.7.0.csv.gz). |
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The final extracted dataset is a JSON file representing a dictionary with language codes and start and end edges for each language. Start edges represent the unique words in a target language, while end edges are the words related to the start edges through various types of relationships. The relationship types and sources are not extracted. |
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### Dataset Structure |
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cn_relations_clean.json: |
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`` |
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{ |
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'language_iso_code_1':{'start_edge_word_1':['end_edge_word_1', 'end_edge_word_2', ...], ...}, |
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... |
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} |
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`` |
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### Licensing Information |
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This work includes data from ConceptNet 5, which was compiled by the |
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Commonsense Computing Initiative. ConceptNet 5 is freely available under |
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the Creative Commons Attribution-ShareAlike license (CC BY SA 3.0) from |
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http://conceptnet.io. |
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### Citation Information |
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``` |
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@paper{speer2017conceptnet, |
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author = {Robyn Speer and Joshua Chin and Catherine Havasi}, |
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title = {ConceptNet 5.5: An Open Multilingual Graph of General Knowledge}, |
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conference = {AAAI Conference on Artificial Intelligence}, |
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year = {2017}, |
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pages = {4444--4451}, |
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keywords = {ConceptNet; knowledge graph; word embeddings}, |
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url = {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972} |
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} |
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``` |
