# kge **Repository Path**: cyrdoco/kge ## Basic Information - **Project Name**: kge - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-12-02 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README #

LibKGE is a PyTorch-based library for efficient training, evaluation, and hyperparameter optimization of [knowledge graph embeddings](https://ieeexplore.ieee.org/document/8047276) (KGE). It is highly configurable, easy to use, and extensible. Other KGE frameworks are [listed below](#other-kge-frameworks). The key goal of LibKGE is to foster *reproducible research* into (as well as meaningful comparisons between) KGE models and training methods. As we argue in our [ICLR 2020 paper](https://github.com/uma-pi1/kge-iclr20) (see [video](https://iclr.cc/virtual_2020/poster_BkxSmlBFvr.html)), the choice of training strategy and hyperparameters are very influential on model performance, often more so than the model class itself. LibKGE aims to provide *clean implementations* of training, hyperparameter optimization, and evaluation strategies that can be used with any model. Every potential knob or heuristic implemented in the framework is exposed explicitly via *well-documented* configuration files (e.g., see [here](kge/config-default.yaml) and [here](kge/model/embedder/lookup_embedder.yaml)). LibKGE also provides the most common KGE models and new ones can be easily added (contributions welcome!). For link prediction tasks, rule-based systems such as [AnyBURL](http://web.informatik.uni-mannheim.de/AnyBURL/) are a competitive alternative to KGE. ## Table of contents 1. [Features](#features) 2. [Results and pretrained models](#results-and-pretrained-models) 3. [Quick start](#quick-start) 4. [Using LibKGE](#using-libkge) 5. [Currently supported KGE models](#currently-supported-kge-models) 6. [Adding a new model](#adding-a-new-model) 7. [Known issues](#known-issues) 8. [Changelog](CHANGELOG.md) 9. [Other KGE frameworks](#other-kge-frameworks) 10. [How to cite](#how-to-cite) ## Features - **Training** - Training types: negative sampling, 1vsAll, KvsAll - Losses: binary cross entropy (BCE), Kullback-Leibler divergence (KL), margin ranking (MR) - All optimizers and learning rate schedulers of PyTorch supported - Early stopping - Checkpointing - Stop (e.g., via `Ctrl-C`) and resume at any time - **Hyperparameter tuning** - Grid search, manual search, quasi-random search (using [Ax](https://ax.dev/)), Bayesian optimization (using [Ax](https://ax.dev/)) - Highly parallelizable (multiple CPUs/GPUs on single machine) - Stop and resume at any time - **Evaluation** - Entity ranking metrics: Mean Reciprocal Rank (MRR), HITS@k with/without filtering - Drill-down by: relation type, relation frequency, head or tail - **Extensive logging** - Logging for training, hyper-parameter tuning and evaluation in machine readable formats to facilitate analysis - **KGE models** - All models can be used with or without reciprocal relations - [RESCAL](http://www.icml-2011.org/papers/438_icmlpaper.pdf) ([code](kge/model/rescal.py), [config](kge/model/rescal.yaml)) - [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data) ([code](kge/model/transe.py), [config](kge/model/transe.yaml)) - [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf) ([code](kge/model/distmult.py), [config](kge/model/distmult.yaml)) - [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) ([code](kge/model/complex.py), [config](kge/model/complex.yaml)) - [ConvE](https://arxiv.org/abs/1707.01476) ([code](kge/model/conve.py), [config](kge/model/conve.yaml)) - [RelationalTucker3](https://arxiv.org/abs/1902.00898)/[TuckER](https://arxiv.org/abs/1901.09590) ([code](kge/model/relational_tucker3.py), [config](kge/model/relational_tucker3.yaml)) - [CP](https://arxiv.org/abs/1806.07297) ([code](kge/model/cp.py), [config](kge/model/cp.yaml)) - [SimplE](https://arxiv.org/abs/1802.04868) ([code](kge/model/simple.py), [config](kge/model/simple.yaml)) - [RotatE](https://arxiv.org/abs/1902.10197) ([code](kge/model/rotate.py), [config](kge/model/rotate.yaml)) - **Embedders** - Lookup embedder ([code](kge/model/embedder/lookup_embedder.py), [config](kge/model/embedder/lookup_embedder.yaml)) - Projection embedder ([code](kge/model/embedder/projection_embedder.py), [config](kge/model/embedder/projection_embedder.yaml)) ## Results and pretrained models We list some example results (filtered MRR and HITS@k on test data) obtained with LibKGE below. These results are obtained by running automatic hyperparameter search as described [here](https://github.com/uma-pi1/kge-iclr20). These results are not necessarily the best results that can be achieved using LibKGE, but they are comparable in that a common experimental setup (and equal amount of work) has been used for hyperparameter optimization for each model. Since we use **filtered MRR for model selection**, our results may not be indicative of the achievable model performance for other validation metrics (such as HITS@10, which has been used for model selection elsewhere). We report performance numbers on the entire test set, **including the triples that contain entities not seen during training**. This is not done consistently throughout existing KGE implementations: some frameworks remove unseen entities from the test set, which leads to a perceived increase in performance (e.g., roughly add +3pp to our WN18RR MRR numbers for this method of evaluation). We also provide pretrained models for these results. Each pretrained model is given in the form of a LibKGE checkpoint, which contains the model as well as additional information (such as the configuration being used). See the documentation below on how to use checkpoints. #### FB15K-237 (Freebase) | | MRR | Hits@1 | Hits@3 | Hits@10 | Config file | Pretrained model | |-------------------------------------------------------------------------------------------------------|------:|-------:|-------:|--------:|-------------------------------------------------------------------------------------------------:|----------------------------------------------------------------------------------------------:| | [RESCAL](http://www.icml-2011.org/papers/438_icmlpaper.pdf) | 0.356 | 0.263 | 0.393 | 0.541 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-rescal.yaml) | [1vsAll-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-rescal.pt) | | [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data) | 0.313 | 0.221 | 0.347 | 0.497 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-transe.yaml) | [NegSamp-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-transe.pt) | | [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf) | 0.343 | 0.250 | 0.378 | 0.531 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-distmult.yaml) | [NegSamp-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-distmult.pt) | | [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) | 0.348 | 0.253 | 0.384 | 0.536 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-complex.yaml) | [NegSamp-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-complex.pt) | | [ConvE](https://arxiv.org/abs/1707.01476) | 0.339 | 0.248 | 0.369 | 0.521 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-conve.yaml) | [1vsAll-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/fb15k-237-conve.pt) | #### WN18RR (Wordnet) | | MRR | Hits@1 | Hits@3 | Hits@10 | Config file | Pretrained model | |-------------------------------------------------------------------------------------------------------|------:|-------:|-------:|--------:|--------------------------------------------------------------------------------------------:|----------------------------------------------------------------------------------------:| | [RESCAL](http://www.icml-2011.org/papers/438_icmlpaper.pdf) | 0.467 | 0.439 | 0.480 | 0.517 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-rescal.yaml) | [KvsAll-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-rescal.pt) | | [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data) | 0.228 | 0.053 | 0.368 | 0.520 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-transe.yaml) | [NegSamp-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-transe.pt) | | [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf) | 0.452 | 0.413 | 0.466 | 0.530 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-distmult.yaml) | [KvsAll-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-distmult.pt) | | [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) | 0.475 | 0.438 | 0.490 | 0.547 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-complex.yaml) | [1vsAll-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-complex.pt) | | [ConvE](https://arxiv.org/abs/1707.01476) | 0.442 | 0.411 | 0.451 | 0.504 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-conve.yaml) | [KvsAll-kl](http://web.informatik.uni-mannheim.de/pi1/iclr2020-models/wnrr-conve.pt) | #### FB15K (Freebase) | | MRR | Hits@1 | Hits@3 | Hits@10 | Config file | Pretrained model | |-------------------------------------------------------------------------------------------------------|------:|-------:|-------:|--------:|-------------------------------------------------------------------------------------------------:|----------------------------------------------------------------------------------------------:| | [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data) | 0.676 | 0.542 | 0.787 | 0.875 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/fb15k-transe.yaml) | [NegSamp-kl](http://web.informatik.uni-mannheim.de/pi1/libkge-models/fb15k-transe.pt) | | [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf) | 0.841 | 0.806 | 0.863 | 0.903 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/fb15k-distmult.yaml) | [1vsAll-kl](http://web.informatik.uni-mannheim.de/pi1/libkge-models/fb15k-distmult.pt) | | [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) | 0.838 | 0.807 | 0.856 | 0.893 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/fb15k-complex.yaml) | [1vsAll-kl](http://web.informatik.uni-mannheim.de/pi1/libkge-models/fb15k-complex.pt) | | [ConvE](https://arxiv.org/abs/1707.01476) | 0.825 | 0.781 | 0.855 | 0.896 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/fb15k-conve.yaml) | [KvsAll-bce](http://web.informatik.uni-mannheim.de/pi1/libkge-models/fb15k-conve.pt) | #### WN18 (Wordnet) | | MRR | Hits@1 | Hits@3 | Hits@10 | Config file | Pretrained model | |-------------------------------------------------------------------------------------------------------|------:|-------:|-------:|--------:|--------------------------------------------------------------------------------------------:|----------------------------------------------------------------------------------------:| | [RESCAL](http://www.icml-2011.org/papers/438_icmlpaper.pdf) | 0.948 | 0.943 | 0.951 | 0.956 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-rescal.yaml) | [1vsAll-kl](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-rescal.pt) | | [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data) | 0.553 | 0.315 | 0.764 | 0.924 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-transe.yaml) | [NegSamp-bce](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-transe.pt) | | [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf) | 0.941 | 0.932 | 0.948 | 0.954 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-distmult.yaml) | [1vsAll-kl](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-distmult.pt) | | [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) | 0.951 | 0.947 | 0.953 | 0.958 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-complex.yaml) | [KvsAll-kl](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-complex.pt) | | [ConvE](https://arxiv.org/abs/1707.01476) | 0.947 | 0.943 | 0.949 | 0.953 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-conve.yaml) | [1vsAll-kl](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wn18-conve.pt) | #### Wikidata5M (Wikidata) LibKGE supports large datasets such as Wikidata5M (4.8M entities). The result given below was found by automatic hyperparameter search similar to the one used for the smaller datasets above, but with some values fixed (training with shared negative sampling, embedding dimension: 128, batch size: 1024, optimizer: Adagrad, regularization: weighted). We ran 30 pseudo-random configurations for 20 epochs, and then reran the configuration that performed best on validation data for 200 epochs. | | MRR | Hits@1 | Hits@3 | Hits@10 | Config file | Pretrained model | |-------------------------------------------------------------|------:|-------:|-------:|--------:|-----------------------------------------------------------------------------------------------:|--------------------------------------------------------------------------------------------:| | [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) | 0.301 | 0.245 | 0.331 | 0.397 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wikidata5m-complex.yaml) | [NegSamp-kl](http://web.informatik.uni-mannheim.de/pi1/libkge-models/wikidata5m-complex.pt) | #### Yago3-10 (YAGO) The result given below was found by the same automatic hyperparameter search used for Wikidata5M. We reran the configuration that performed best on validation data for 400 epochs. | | MRR | Hits@1 | Hits@3 | Hits@10 | Config file | Pretrained model | |-------------------------------------------------------------|------:|-------:|-------:|--------:|-----------------------------------------------------------------------------------------------:|--------------------------------------------------------------------------------------------:| | [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) | 0.551 | 0.476 | 0.596 | 0.682 | [config.yaml](http://web.informatik.uni-mannheim.de/pi1/libkge-models/yago3-10-complex.yaml) | [NegSamp-kl](http://web.informatik.uni-mannheim.de/pi1/libkge-models/yago3-10-complex.pt) | ## Quick start ```sh # retrieve and install project in development mode git clone https://github.com/uma-pi1/kge.git cd kge pip install -e . # download and preprocess datasets cd data sh download_all.sh cd .. # train an example model on toy dataset (you can omit '--job.device cpu' when you have a gpu) kge start examples/toy-complex-train.yaml --job.device cpu ``` ## Using LibKGE LibKGE supports training, evaluation, and hyperparameter tuning of KGE models. The settings for each task can be specified with a configuration file in YAML format or on the command line. The default values and usage for available settings can be found in [config-default.yaml](kge/config-default.yaml) as well as the model- and embedder-specific configuration files (such as [lookup_embedder.yaml](kge/model/embedder/lookup_embedder.yaml)). #### Train a model First create a configuration file such as: ```yaml job.type: train dataset.name: fb15k-237 train: optimizer: Adagrad optimizer_args: lr: 0.2 valid: every: 5 metric: mean_reciprocal_rank_filtered model: complex lookup_embedder: dim: 100 regularize_weight: 0.8e-7 ``` To begin training, run one of the following: ```sh # Store the file as `config.yaml` in a new folder of your choice. Then initiate or resume # the training job using: kge resume # Alternatively, store the configuration anywhere and use the start command # to create a new folder # /local/experiments/- # with that config and start training there. kge start # In both cases, configuration options can be modified on the command line, too: e.g., kge start config.yaml --job.device cuda:0 --train.optimizer Adam ``` Various checkpoints (including model parameters and configuration options) will be created during training. These checkpoints can be used to resume training (or any other job type such as hyperparameter search jobs). #### Resume training All of LibKGE's jobs can be interrupted (e.g., via `Ctrl-C`) and resumed (from one of its checkpoints). To resume a job, use: ```sh kge resume # Change the device when resuming kge resume --job.device cuda:1 ``` By default, the last checkpoint file is used. The filename of the checkpoint can be overwritten using ``--checkpoint``. #### Evaluate a trained model To evaluate trained model, run the following: ```sh # Evaluate a model on the validation split kge valid # Evaluate a model on the test split kge test ``` By default, the checkpoint file named ``checkpoint_best.pt`` (which stores the best validation result so far) is used. The filename of the checkpoint can be overwritten using ``--checkpoint``. #### Hyperparameter optimization LibKGE supports various forms of hyperparameter optimization such as grid search or Bayesian optimization. The search type and search space are specified in the configuration file. For example, you may use [Ax](https://ax.dev/) for SOBOL (pseudo-random) and Bayesian optimization. The following config file defines a search of 10 SOBOL trials (arms) followed by 20 Bayesian optimization trials: ```yaml job.type: search search.type: ax dataset.name: wnrr model: complex valid.metric: mean_reciprocal_rank_filtered ax_search: num_trials: 30 num_sobol_trials: 10 # remaining trials are Bayesian parameters: - name: train.batch_size type: choice values: [256, 512, 1024] - name: train.optimizer_args.lr type: range bounds: [0.0003, 1.0] - name: train.type type: fixed value: 1vsAll ``` Trials can be run in parallel across several devices: ```sh # Run 4 trials in parallel evenly distributed across two GPUs kge resume --search.device_pool cuda:0,cuda:1 --search.num_workers 4 # Run 3 trials in parallel, with per GPUs capacity kge resume --search.device_pool cuda:0,cuda:1,cuda:1 --search.num_workers 3 ``` #### Export and analyze logs and checkpoints Extensive logs are stored as YAML files (hyperparameter search, training, validation). LibKGE provides a convenience methods to export the log data to CSV. ```sh kge dump trace ``` The command above yields CSV output such as [this output for a training job](docs/examples/dump-example-model.csv) or [this output for a search job](https://github.com/uma-pi1/kge-iclr20/blob/master/data_dumps/iclr2020-fb15k-237-all-trials.csv). Additional configuration options or metrics can be added to the CSV files as needed (using a [keys file](https://github.com/uma-pi1/kge-iclr20/blob/master/scripts/iclr2020_keys.conf)). Information about a checkpoint (such as the configuration that was used, training loss, validation metrics, or explored hyperparameter configurations) can also be exported from the command line (as YAML): ```sh kge dump checkpoint ``` Configuration files can also be dumped in various formats. ```sh # dump just the configuration options that are different from the default values kge dump config # dump the configuration as is kge dump config --raw # dump the expanded config including all configuration keys kge dump config --full ``` #### Help and other commands ```sh # help on all commands kge --help # help on a specific command kge dump --help ``` #### Use a pretrained model in an application Using a trained model trained with LibKGE is straightforward. In the following example, we load a checkpoint and predict the most suitable object for a two subject-relations pairs: ('Dominican Republic', 'has form of government', ?) and ('Mighty Morphin Power Rangers', 'is tv show with actor', ?). ```python import torch from kge.model import KgeModel from kge.util.io import load_checkpoint # download link for this checkpoint given under results above checkpoint = load_checkpoint('fb15k-237-rescal.pt') model = KgeModel.create_from(checkpoint) s = torch.Tensor([0, 2,]).long() # subject indexes p = torch.Tensor([0, 1,]).long() # relation indexes scores = model.score_sp(s, p) # scores of all objects for (s,p,?) o = torch.argmax(scores, dim=-1) # index of highest-scoring objects print(o) print(model.dataset.entity_strings(s)) # convert indexes to mentions print(model.dataset.relation_strings(p)) print(model.dataset.entity_strings(o)) # Output (slightly revised for readability): # # tensor([8399, 8855]) # ['Dominican Republic' 'Mighty Morphin Power Rangers'] # ['has form of government' 'is tv show with actor'] # ['Republic' 'Johnny Yong Bosch'] ``` For other scoring functions (score_sp, score_po, score_so, score_spo), see [KgeModel](kge/model/kge_model.py#L455). ## Currently supported KGE models LibKGE currently implements the following KGE models: - [RESCAL](http://www.icml-2011.org/papers/438_icmlpaper.pdf) ([code](kge/model/rescal.py), [config](kge/model/rescal.yaml)) - [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data) ([code](kge/model/transe.py), [config](kge/model/transe.yaml)) - [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf) ([code](kge/model/distmult.py), [config](kge/model/distmult.yaml)) - [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf) ([code](kge/model/complex.py), [config](kge/model/complex.yaml)) - [ConvE](https://arxiv.org/abs/1707.01476) ([code](kge/model/conve.py), [config](kge/model/conve.yaml)) - [RelationalTucker3](https://arxiv.org/abs/1902.00898) ([code](kge/model/relational_tucker3.py), [config](kge/model/relational_tucker3.yaml)) - [CP](https://arxiv.org/abs/1806.07297) ([code](kge/model/cp.py), [config](kge/model/cp.yaml)) - [SimplE](https://arxiv.org/abs/1802.04868) ([code](kge/model/simple.py), [config](kge/model/simple.yaml)) - [RelationalTucker3](https://arxiv.org/abs/1902.00898)/[TuckER](https://arxiv.org/abs/1901.09590) ([code](kge/model/relational_tucker3.py), [config](kge/model/relational_tucker3.yaml)) - [RotatE](https://arxiv.org/abs/1902.10197) ([code](kge/model/rotate.py), [config](kge/model/rotate.yaml)) The [examples](examples) folder contains some configuration files as examples of how to train these models. We welcome contributions to expand the list of supported models! Please see [CONTRIBUTING](CONTRIBUTING.md) for details and feel free to initially open an issue. ## Adding a new model or embedder To add a new model to LibKGE, extend the [KgeModel](https://github.com/uma-pi1/kge/blob/1c69d8a6579d10e9d9c483994941db97e04f99b3/kge/model/kge_model.py#L243) class. A model is made up of a [KgeEmbedder](https://github.com/uma-pi1/kge/blob/1c69d8a6579d10e9d9c483994941db97e04f99b3/kge/model/kge_model.py#L170) to associate each subject, relation and object to an embedding, and a [KgeScorer](https://github.com/uma-pi1/kge/blob/1c69d8a6579d10e9d9c483994941db97e04f99b3/kge/model/kge_model.py#L76) to score triples given their embeddings. The model implementation should be stored under `/kge/model/.py`, its configuration options under `/kge/model/.yaml` and its import has to be added to `/kge/model/__init__.py`. The embdedder implementation should be stored under `/kge/model/embedder/.py`, its configuration options under `/kge/model/embedder/.yaml` and its import has to be added to `/kge/model/__init__.py`. ## Known issues ## Changelog See [here](CHANGELOG.md). ## Other KGE frameworks Other KGE frameworks: - [Graphvite](https://graphvite.io/) - [AmpliGraph](https://github.com/Accenture/AmpliGraph) - [OpenKE](https://github.com/thunlp/OpenKE) - [PyKEEN](https://github.com/SmartDataAnalytics/PyKEEN) KGE projects for publications that also implement a few models: - [ConvE](https://github.com/TimDettmers/ConvE) - [KBC](https://github.com/facebookresearch/kbc) PRs to this list are welcome. ## How to cite Please cite the following publication to refer to the experimental study about the impact of training methods on KGE performance: ``` @inproceedings{ ruffinelli2020you, title={You {CAN} Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings}, author={Daniel Ruffinelli and Samuel Broscheit and Rainer Gemulla}, booktitle={International Conference on Learning Representations}, year={2020}, url={https://openreview.net/forum?id=BkxSmlBFvr} } ``` If you use LibKGE, please cite the following publication: ``` @inproceedings{ libkge, title="{L}ib{KGE} - {A} Knowledge Graph Embedding Library for Reproducible Research", author={Samuel Broscheit and Daniel Ruffinelli and Adrian Kochsiek and Patrick Betz and Rainer Gemulla}", booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, year={2020}, url={https://www.aclweb.org/anthology/2020.emnlp-demos.22}, pages = "165--174", } ```