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--- |
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license: agpl-3.0 |
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library_name: pytorch |
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base_model: |
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- Qwen/Qwen3-VL-4B-Instruct |
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tags: |
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- rgcn |
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- embedding |
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- onnx |
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--- |
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# EduGraph Embed |
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This model generates embeddings for labels from the |
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[EduGraph Ontology](httpss://github.com/christian-bick/edugraph-ontology). |
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When combined with an [EduGraph Classification Model](httpss://github.com/christian-bick/edugraph-classify-qwen3vl), |
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we can determine similarity between any type of learning content covered by the EduGraph ontology. |
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For example, in tandem, the two models can determine whether some content of a math learning app |
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trains the exact same set of skills tested in a paper quiz, by providing nothing else than a screenshot |
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and a photo. |
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## How it works |
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The model determines similarity based on the *structure* of the EduGraph Ontology. It respects |
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various types of entity relationships to determine similarity, most importantly, parent-child and sibling |
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relationships within the graph in addition to the semantic similarity of their definitions. |
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For example, the model will reliably place labels like `IntegerAddition` and `FractionAddition` |
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closer together than, say, `ShapeIdentification`. |
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To accomplish this, the model generates knowledge graph embeddings that |
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map the ontology structure into a high-dimensional vector space using a |
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[Relational Graph Convolutional Network (R-GCN)](httpss://arxiv.org/abs/1703.06103). |
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## Limitations |
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This model is centered around the EduGraph ontology. The embedding model was trained |
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on the entities and relationships in this ontology. Consequently, it can only embed |
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labels that are defined as entities within this ontology. |
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## Risks |
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**Important:** Currently this model is in a research status and has not been evaluated under real-world conditions. |
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* **ONLY use this model for research, experimentation and evaluation** |
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* **Do NOT use in a classroom environment** |
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* **Do NOT use for automations that might impact children** |
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## Using the Model |
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### Preparation |
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1) Download the following files: |
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- `embed_entities_biased.onnx` |
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- `embed_entities.pt` |
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2) Install the following dependencies: |
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- `torch` |
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- `numpy` |
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- `onnxruntime` |
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### Reference Example |
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See [entity_embeddings_infer.py](httpss://github.com/christian-bick/edugraph-embed/blob/master/src/edugraph/embed/entity_embeddings_infer.py) |
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for reference usage. |
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## License |
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This project is licensed under the GNU Affero General Public License. See the [LICENSE](LICENSE) file for details. |
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If these license terms are not working for you, then get in touch, and we can discuss your options. |
