Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs

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Deep Learningcqd
Overview

Continuous Query Decomposition

This repository contains the official implementation for our ICLR 2021 (Oral) paper, Complex Query Answering with Neural Link Predictors:

@inproceedings{
    arakelyan2021complex,
    title={Complex Query Answering with Neural Link Predictors},
    author={Erik Arakelyan and Daniel Daza and Pasquale Minervini and Michael Cochez},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=Mos9F9kDwkz}
}

In this work we present CQD, a method that reuses a pretrained link predictor to answer complex queries, by scoring atom predicates independently and aggregating the scores via t-norms and t-conorms.

Our code is based on an implementation of ComplEx-N3 available here.

Please follow the instructions next to reproduce the results in our experiments.

1. Install the requirements

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-data.tgz

3. Alternative -- Train your own models

To obtain entity and relation embeddings, we use ComplEx. Use the next commands to train the embeddings for each dataset.

FB15k

% python -m kbc.learn data/FB15k --rank 1000 --reg 0.01 --max_epochs 100  --batch_size 100

FB15k-237

% python -m kbc.learn data/FB15k-237 --rank 1000 --reg 0.05 --max_epochs 100  --batch_size 1000

NELL

% python -m kbc.learn data/NELL --rank 1000 --reg 0.05 --max_epochs 100  --batch_size 1000

Once training is done, the models will be saved in the models directory.

4. Answering queries with CQD

CQD can answer complex queries via continuous (CQD-CO) or combinatorial optimisation (CQD-Beam).

CQD-Beam

Use the kbc.cqd_beam script to answer queries, providing the path to the dataset, and the saved link predictor trained in the previous step. For example,

% python -m kbc.cqd_beam --model_path models/[model_filename].pt

Example:

% PYTHONPATH=. python3 kbc/cqd_beam.py \
  --model_path models/FB15k-model-rank-1000-epoch-100-*.pt \
  --dataset FB15K --mode test --t_norm product --candidates 64 \
  --scores_normalize 0 data/FB15k

models/FB15k-model-rank-1000-epoch-100-1602520745.pt FB15k product 64
ComplEx(
  (embeddings): ModuleList(
    (0): Embedding(14951, 2000, sparse=True)
    (1): Embedding(2690, 2000, sparse=True)
  )
)

[..]

This will save a series of JSON fils with results, e.g.

% cat "topk_d=FB15k_t=product_e=2_2_rank=1000_k=64_sn=0.json"
{
  "MRRm_new": 0.7542805715523118,
  "MRm_new": 50.71081983144581,
  "[email protected]_new": 0.6896709378392843,
  "[email protected]_new": 0.7955001359095913,
  "[email protected]_new": 0.8676865172456019
}

CQD-CO

Use the kbc.cqd_co script to answer queries, providing the path to the dataset, and the saved link predictor trained in the previous step. For example,

% python -m kbc.cqd_co data/FB15k --model_path models/[model_filename].pt --chain_type 1_2

Final Results

All results from the paper can be produced as follows:

% cd results/topk
% ../topk-parse.py *.json | grep rank=1000
d=FB15K rank=1000 & 0.779 & 0.584 & 0.796 & 0.837 & 0.377 & 0.658 & 0.839 & 0.355
d=FB237 rank=1000 & 0.279 & 0.219 & 0.352 & 0.457 & 0.129 & 0.249 & 0.284 & 0.128
d=NELL rank=1000 & 0.343 & 0.297 & 0.410 & 0.529 & 0.168 & 0.283 & 0.536 & 0.157
% cd ../cont
% ../cont-parse.py *.json | grep rank=1000
d=FB15k rank=1000 & 0.454 & 0.191 & 0.796 & 0.837 & 0.336 & 0.513 & 0.816 & 0.319
d=FB15k-237 rank=1000 & 0.213 & 0.131 & 0.352 & 0.457 & 0.146 & 0.222 & 0.281 & 0.132
d=NELL rank=1000 & 0.265 & 0.220 & 0.410 & 0.529 & 0.196 & 0.302 & 0.531 & 0.194
Owner
UCL Natural Language Processing
UCL Natural Language Processing
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