Query Answering In Knowledge Space

This Code is the implementation of "Query Answering with Knowledge Graph Embeddings via Continuous Optimisation" inspired from "Querying Complex Networks in Vector Space"

The implementation builds upon and expands the code-base from Canonical Tensor Decomposition for Knowledge Base Completion from FAIR.

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Setup

The Setup process is similar the the original repo as the process for installation has been altered sloghtly to accomodate fror the expanded functional

1) Create an environemnt and install the package and the requirements

conda create --name kbc_env python=3.7
source activate kbc_env
conda install --file requirements.txt -c pytorch
pip install requirements.txt
python setup.py install

Here conda environments can be replaced manual virtualenv environments.

2a) Download the data used in original KBC

chmod +x download_data.sh
./download_data.sh

2b) Download the Bio Data used as the benchmark for "Querying Complex Networks in Vector Space"

The Bio data can be downloaded here

Unzip the data under the name "Bio" in the folder "/Query-Answering-In-Knowledge-Space/kbc/src_data"

2c) Convert and Process the Bio data to appropriate form.

python kbc/bio_data_process.py

3) Process all of the datasets to a Knowledge Graph format.

python kbc/process_datasets.py

Training

You can train a KBC model on any one of the datasets processed in the previous step

Example:

python kbc/learn.py --dataset Bio --model ComplEx --max_epochs 30 \
--model_save_schedule 10 --valid 10 --reg 5e-2 --batch_size 1024 --rank 128

All of the input parameters and options can be found in both the code documentation or simply by using -help in the command line.

Optimization and Benchmarks

Sampling chains

To Sample longer chains of different types as described in the paper(TODO: Add link), use

Example:

python kbc/chain_dataset.py --dataset Bio --threshold 5000

Optimizing

Find the target vectors and answer the queries as described in the paper(TODO: Add link). (Reproduce the benchmarks this way)

python kbc/query_space_optimize.py --model_path models/Bio-model-epoch-30-1566308599.pt \
--dataset Bio --dataset_mode test --similarity_metric l2 --chain_type 1_3

Results

	Bio	Bio		WN18
	GQE	Continuous Optimization with KG embeddings. (Our solution)		Continuous Optimization with KG embeddings. (Our solution)
Type-1	0.99	0.99		0.987742
Type1-2	0.931927	0.95737		0.996718
Type2-2	0.925571	0.96639		0.996269
Type1-3	0.89373	0.96461		0.9985055
Type2-3	0.881848	0.93691		0.9907173
Type3-3	0.87890	0.93952		0.99603959
Type4-3	0.886478	0.85132		0.99331019

	Hits@1	Hits@1
	Bio	WN18
Type 1	0.561	0.9774
Type 1-2	0.704	0.8642
Type 2-2	0.599	0.9336
Type 1-3	0.629	0.9312
Type 2-3	0.599	0.8868
Type 3-3	0.582	0.8112
Type 4-3	0.503	0.8176

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TODO

• Refactor the code into more general functions for different chains types in models.py and query_space_optimize.py