Convolutional Complex Knowledge Graph Embeddings

This open-source project contains the Pytorch implementation of our approach (ConEx), training and evaluation scripts. We added ConEx and its variant AConEx into DICE Embeddings Framework open-source project to ease the deployment and the distributed computing. Therein, we provided pre-trained models on many knowledge graphs

Link Prediction Results

In the below, we provide a brief overview of the link prediction results. Results are sorted in descending order of the size of the respective dataset.

YAGO3-10

	MRR	Hits@10	Hits@3	Hits@1
DistMult	.340	.540	.380	.240
ComplEx	.360	.550	.400	.260
ConvE	.400	.620	.490	.350
HypER	.530	.680	.580	.460
RotatE	.500	.670	.550	.400
ConEx	.553	.696	.601	.477

FB15K-237 (Freebase)

(*) denotes the newly reported link prediction results.

	MRR	Hits@10	Hits@3	Hits@1
DistMult	.241	.419	.263	.155
ComplEx	.247	.428	.275	.158
ConvE	.335	.501	.356	.237
RESCAL*	.357	.541	.393	.263
DistMult*	.343	.531	.378	.250
ComplEx*	.348	.536	.384	.253
ConvE*	.339	.521	.369	.248
HypER	.341	.520	.376	.252
NKGE	.330	.510	.365	.241
RotatE	.338	.533	.375	.241
TuckER	.358	.544	.394	.266
QuatE	.366	.556	.401	.271
ConEx	.366	.555	.403	.271
Ensemble.ConEx	.376	.570	.415	.279

WN18RR (Wordnet)

(*) denotes the newly reported link prediction results.

	MRR	Hits@10	Hits@3	Hits@1
DistMult	.430	.490	.440	.390
ComplEx	.440	.510	.460	.410
ConvE	.430	.520	.440	.400
RESCAL*	.467	.517	.480	.439
DistMult*	.452	.530	.466	.413
ComplEx*	.475	.547	.490	.438
ConvE*	.442	.504	.451	.411
HypER	.465	.522	.477	.436
NKGE	.450	.526	.465	.421
RotatE	.476	.571	.492	.428
TuckER	.470	.526	.482	.443
QuatE	.482	.572	.499	.436
ConEx	.481	.550	.493	.448
Ensemble.ConEx	.485	.559	.495	.450

WN18RR* dataset

We spot flaws on WN18RR, FB15K-237 and YAGO3-10. More specifically, the validation and test splits of the dataset contain entities that do not occur in the training split. We refer Out-of-Vocabulary Entities in Link Prediction for more details.

	MRR	Hits@10	Hits@3	Hits@1
DistMult-ComplEx	.475	.579	.497	.426
DistMult-TuckER	.476	.569	.492	.433
ConEx-DistMult	.484	.580	.501	.439
ConEx-ComplEx	.501	.589	.518	.456
ConEx-TuckER	.514	.583	.526	.479
Ensemble.ConEx	.517	.594	.526	.479

Visualisation of Embeddings

A 2D PCA projection of relation embeddings on the FB15K-237 dataset. The Figure shows that inverse relations cluster in distant regions. Note that we applied the standard data augmentation technique To generate inverse relations, relations are renamed by adding suffix of inverse as done in~\cite{balavzevic2019tucker}.

Installation

First clone the repository:

git clone https://github.com/dice-group/Convolutional-Complex-Knowledge-Graph-Embeddings.git

Then obtain the required libraries:

conda env create -f environment.yml
source activate conex

The code is compatible with Python 3.6.4.

Usage

• run_script.py can be used to train ConEx on a desired dataset.
• grid_search.py can be used to rerun our experiments.

Pre-trained Models

Please contact: caglar.demir@upb.de, if you wish to obtain ConEx embeddings of specific dataset.

• Forte embeddings
• Hepatitis embeddings
• Lymphography embeddings
• Mammographic embeddings
• Animals embeddings
• YAGO3-10 embeddings
• FB15K-237 embeddings
• FB15K embeddings
• WN18RR embeddings
• WN18 embeddings

Reproduce link prediction results

Please follow the next steps to reproduce all reported results.

• Unzip the datasets: unzip KGs.zip
• Create a folder for pretrained models: mkdir PretrainedModels
• Download pretrained models via hobbitdata into PretrainedModels.
• python reproduce_lp.py reproduces link prediction results on the FB15K-237, FB15K, WN18, WN18RR and YAGO3-10 benchmark datasets.
• python reproduce_baselines.py reproduces link prediction results of DistMult, ComplEx and TuckER on the FB15K-237, WN18RR and YAGO3-10 benchmark datasets.
• settings.json files store the hyperparameter setting for each model.
• python reproduce_ensemble.py reports link prediction results of ensembled models.
• python reproduce_lp_new.py reports link prediction results on WN18RR*, FB15K-237* and YAGO3-10*.
• python reproduce_ablation.py.py reports link prediction results of our ablation study.

Link Prediction Results

In the below, we provide a brief overview of the link prediction results.

YAGO3-10

	MRR	Hits@10	Hits@3	Hits@1
DistMult	.340	.540	.380	.240
ComplEx	.360	.550	.400	.260
ConvE	.440	.620	.490	.350
HypER	.530	.678	.580	.455
RotatE	.495	.670	.550	.400
DistMult	.543	.683	.590	.466
ComplEx	.547	.690	.594	.468
TuckER	.427	.609	.476	.331
ConEx	.553	.696	.601	.474

FB15K-237

	MRR	Hits@10	Hits@3	Hits@1
DistMult	.241	.419	.263	.155
ComplEx	.247	.428	.275	.158
ConvE	.335	.501	.356	.237
DistMult	.343	.531	.378	.250
ComplEx	.348	.536	.384	.253
ConvE	.339	.521	.369	.248
RotatE	.338	.533	.375	.241
HypER	.341	.520	.376	.252
DistMult	.353	.539	.390	.260
ComplEx	.332	.509	.366	.244
TuckER	.363	.553	.400	.268
ConEx	.366	.555	.403	.271
Ensemble of ConEx	.376	.570	.415	.279

Acknowledgement

We based our implementation on the open source implementation of TuckER. We would like to thank for the readable codebase.

How to cite

@inproceedings{demir2021convolutional,
title={Convolutional Complex Knowledge Graph Embeddings},
author={Caglar Demir and Axel-Cyrille Ngonga Ngomo},
booktitle={Eighteenth Extended Semantic Web Conference - Research Track},
year={2021},
url={https://openreview.net/forum?id=6T45-4TFqaX}}

For any further questions or suggestions, please contact: caglar.demir@upb.de or caglardemir8@gmail.com