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Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE

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Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE

For the sake of reproducibility, resources for replicating the experiments presented in our paper are provided below.

Datasets

The datasets/ folder contains the following datasets: YAGO14K, FB15k187, and DBpedia77k [1].

Statistics for these datasets as well as their corresponding protographs are reported in the following two tables.

Dataset #Classes #HierarchyDepth #Entities #Relations #Triples (train) #Triples (valid) #Triples (test)
YAGO14k 954 5 14,178 37 18,263 472 448
FB15k187 624 2 14,305 187 245,350 15,256 17,830
DBpedia77k 280 8 76,651 150 140,760 16,334 32,934
Dataset Protograph #Entities #Relations #Triples
YAGO14K P1 22 37 37
P2 590 37 4,959
FB15k187 P1 138 187 187
P2 138 187 187
DBpedia77k P1 55 150 150
P2 186 150 3,210

Building Protographs

Two heuristics for building protographs are presented in our paper. In order to build the required protographs for YAGO14K, FB15k187 (renamed as FB14K for short), and DBpedia77k (DB77K for short) at the same time, please run the following commands:

python get_prototype.py --dataset YAGO14K && python get_prototype.py --dataset FB14K && python get_prototype.py --dataset DB77K

Note that you can bring your own datasets (with all the required files) and run the following command:

python get_prototype.py --dataset mydataset

Knowledge Graph Embeddings

Pre-trained embeddings' files are provided in the datasets/ folder. These correspond to the embeddings found at the best epoch on the validation, for each combination of model, setting, and dataset. In particular, for each dataset the MASCHInE-P1/ (resp. MASCHInE-P2/) folder contain embeddings of the best models after the fine-tuning step.

We also made our scripts for training and testing available. These will be refactored upon acceptance. In particular, the _vanilla/ folder contains all the necessary files to train and test knowledge graph embedding models in the vanilla setting. The _transfer/ folder has the same purpose, but for training and testing MASCHInE-P1 and MASCHInE-P2. Before using these scripts, you should first place them at the root of this repo (i.e. in their parent folder).

Hyperparameters

Below are reported the best hyperparameters found, which were used for training models:

YAGO14K dimension learning rate batch size regularizer regularizer weight
TransE 100 0.001 2048 L2 0.001
DistMult 100 0.001 2048 L2 0.0001
ComplEx 100 0.01 2048 L2 0.1
ConvE 200 0.001 512 None None
TuckER 200 0.001 128 None None
FB15k187 dimension learning rate batch size regularizer regularizer weight
TransE 200 0.001 2048 L2 0.001
DistMult 200 0.001 2048 L2 0.01
ComplEx 200 0.001 2048 L2 0.1
ConvE 200 0.001 128 None None
TuckER 200 0.0005 128 None None
DBpedia77K dimension learning rate batch size regularizer regularizer weight
TransE 200 0.001 2048 L2 0.001
DistMult 200 0.001 2048 L2 0.01
ComplEx 200 0.001 2048 L2 0.1
ConvE 200 0.001 512 None None
TuckER 200 0.001 128 None None

Link Prediction

Link prediction experiments can be replicated using the code provided in the _vanilla/ and _transfer/ folders.

Entity Clustering

Clustering experiments are performed following the guidelines and code provided in https://github.com/mariaangelapellegrino/Evaluation-Framework [2].

Node Classification

Node classification experiments are performed following the guidelines and code provided in https://github.com/janothan/DL-TC-Generator [3].

References

[1] Hubert, N., Monnin, P., Brun, A., & Monticolo, D. (2023). Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction.

[2] Pellegrino, M. A., Cochez, M., Garofalo, M., & Ristoski, P. (2019). A configurable evaluation framework for node embedding techniques. In The Semantic Web: ESWC 2019 Satellite Events: ESWC 2019 Satellite Events, Portorož, Slovenia, June 2–6, 2019, Revised Selected Papers 16 (pp. 156-160). Springer International Publishing.

[3] Portisch, J., & Paulheim, H. (2022, October). The DLCC node classification benchmark for analyzing knowledge graph embeddings. In The Semantic Web–ISWC 2022: 21st International Semantic Web Conference, Virtual Event, October 23–27, 2022, Proceedings (pp. 592-609). Cham: Springer International Publishing.