---
license: cc-by-4.0
---

# Clean ConceptNet Data for All Languages

## Data Details

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).

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.

### Dataset Structure

cn_relations_clean.json:

``
{
'language_iso_code_1':{'start_edge_word_1':['end_edge_word_1', 'end_edge_word_2', ...], ...},
...
}
``

### Licensing Information

This work includes data from ConceptNet 5, which was compiled by the
Commonsense Computing Initiative. ConceptNet 5 is freely available under
the Creative Commons Attribution-ShareAlike license (CC BY SA 3.0) from
http://conceptnet.io.

### Citation Information

```
@paper{speer2017conceptnet,
    author = {Robyn Speer and Joshua Chin and Catherine Havasi},
    title = {ConceptNet 5.5: An Open Multilingual Graph of General Knowledge},
    conference = {AAAI Conference on Artificial Intelligence},
    year = {2017},
    pages = {4444--4451},
    keywords = {ConceptNet; knowledge graph; word embeddings},
    url = {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972}
}
```