Var2Vec

The code is for our AAAI2023 paper: Efficient Embeddings of Logical Variables for Query Answering over Incomplete Knowledge Graphs (To appear) by Dingmin Wang, Yeyuan Chen and Bernardo Cuenca Grau.

1. Install the requirements

Our implementation is based on CQD (Complex Query Answering with Neural Link Predictors) repo at this link.

We recommend creating a new environment:

% conda create --name cqd python=3.8 && conda activate cqd
% pip install -r requirements.txt

2. Download the data

We use 3 knowledge graphs: FB15k, FB15k-237, and NELL. From the root of the repository, download and extract the files to obtain the folder data, containing the sets of triples and queries for each graph.

% wget http://data.neuralnoise.com/cqd-data.tgz
% tar xvf cqd-data.tgz

3. Download the models

Then you need neural link prediction models -- one for each of the datasets. Our pre-trained neural link prediction models are available here:

% wget http://data.neuralnoise.com/cqd-models.tgz
% tar xvf cqd-models.tgz

4. Train your model based on existing link predictors.

Then you can do the following to freeze link predictor and train the efficient matrix.

First, look at the tenth line of kbc/models.py，change this address to you own working folder

Then you can run bash test.sh to train your models. These models will be put into models/converter

5. Answering queries with our method

You can refer to test_final.py to answer queries with different types and models.

For example:

['FB15k-model-rank-1000-epoch-100-1602520745.pt','FB15k','True','1_1','0.0','0.0','0.1','0']

It encodes the location of your model (address that is relative to models/converter), and the dataset you want to use. 1_1 means you want to answer 1p queries. Other items are hyperparameters explained in our code.

To reproduce our reported result, please run test_final.py directly.