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zjunlp / KNN-KG

[NLPCC 2023] Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings with Language Models
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fb15k-237 fb15k237 inductive inductive-reasoning kg kge knn knn-kge knnkg knowledge-graph knowledge-graph-embeddings machine-learning semiparametric-models transductive transductive-reasoning wn18rr

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KNN-KG

Code for the NLPCC2023 paper "Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings with Language Models".

Requirements

To install requirements:

pip install -r requirements.txt

Run the experiments

Training

Entity Embedding Initialization

Use the command below to add entities to BERT and train the entity embedding layer to use in the later training. For another dataset WN18RR just replacing the dataset name will be fine.

./scripts/pretrain_fb15k.sh

The parameters of Entity Embedding Layer trained will be used in the next Entity prediction task.

Entity Prediction Task

Use the command below to train the model to predict the correct entity in the masked position.

./scripts/fb15k-237/fb15k.sh

Consturct Knowledge Store

After training the model in Entity prediction task, we use the model to get the knowledge store built from triples and descriptions.

./scripts/fb15k-237/get_knowledge_store.sh

Inference

Here we have a trained model and our knowledge store (e.g., faiss.dump file), use the command below to inference in the test set.

./scripts/fb15k-237/inference.sh

And for inductive setting, the command is similar to the transductive setting (just replace the dataset with inductive dataset), the code will automatically handle the differences.