DICE Embeddings: Hardware-agnostic Framework for Large-scale Knowledge Graph Embeddings

Knowledge graph embedding research has mainly focused on learning continuous representations of knowledge graphs towards the link prediction problem. Recently developed frameworks can be effectively applied in a wide range of research-related applications. Yet, using these frameworks in real-world applications becomes more challenging as the size of the knowledge graph grows.

We developed the DICE Embeddings framework (dicee) to compute embeddings for large-scale knowledge graphs in a hardware-agnostic manner. To achieve this goal, we rely on

Pandas & Co. to use parallelism at preprocessing a large knowledge graph,
PyTorch & Co. to learn knowledge graph embeddings via multi-CPUs, GPUs, TPUs or computing cluster, and
Huggingface to ease the deployment of pre-trained models.

Why Pandas & Co. ? A large knowledge graph can be read and preprocessed (e.g. removing literals) by pandas, modin, or polars in parallel. Through polars, a knowledge graph having more than 1 billion triples can be read in parallel fashion. Importantly, using these frameworks allow us to perform all necessary computations on a single CPU as well as a cluster of computers.

Why PyTorch & Co. ? PyTorch is one of the most popular machine learning frameworks available at the time of writing. PytorchLightning facilitates scaling the training procedure of PyTorch without boilerplate. In our framework, we combine PyTorch & PytorchLightning. Users can choose the trainer class (e.g., DDP by Pytorch) to train large knowledge graph embedding models with billions of parameters. PytorchLightning allows us to use state-of-the-art model parallelism techniques (e.g. Fully Sharded Training, FairScale, or DeepSpeed) without extra effort. With our framework, practitioners can directly use PytorchLightning for model parallelism to train gigantic embedding models.

Why Hugging-face Gradio? Deploy a pre-trained embedding model without writing a single line of code.

For more please visit dice-embeddings!

Installation

Click me!

Installation from Source

git clone https://github.com/dice-group/dice-embeddings.git
conda create -n dice python=3.10.13 --no-default-packages && conda activate dice && pip3 install -e .
# or
pip3 install -e .["dev"]

or

pip install dicee

Download Knowledge Graphs

wget https://files.dice-research.org/datasets/dice-embeddings/KGs.zip --no-check-certificate && unzip KGs.zip

To test the Installation

python -m pytest -p no:warnings -x # Runs >119 tests leading to > 15 mins
python -m pytest -p no:warnings --lf # run only the last failed test
python -m pytest -p no:warnings --ff # to run the failures first and then the rest of the tests.

Knowledge Graph Embedding Models

To see available Models

--model Decal | Keci | DualE | ComplEx | QMult | OMult | ConvQ | ConvO | ConEx | TransE | DistMult | Shallom
--model Pykeen_QuatE | Pykeen_Mure all embedding models available in https://github.com/pykeen/pykeen#models can be selected.

Training and scoring techniques

--trainer torchCPUTrainer | PL | MP | torchDDP
--scoring_technique 1vsAll | KvsAll | AllvsAll | KvsSample | NegSample

How to Train

To see a code snippet

Training Techniques

A KGE model can be trained with a state-of-the-art training technique --trainer "torchCPUTrainer" | "PL" | "MP" | torchDDP

# CPU training
dicee --dataset_dir "KGs/UMLS" --trainer "torchCPUTrainer" --scoring_technique KvsAll --model "Keci" --eval_model "train_val_test"
# Distributed Data Parallelism
dicee --dataset_dir "KGs/UMLS" --trainer "PL" --scoring_technique KvsAll --model "Keci" --eval_model "train_val_test"
# Model Parallelism
dicee --dataset_dir "KGs/UMLS" --trainer "MP" --scoring_technique KvsAll --model "Keci" --eval_model "train_val_test"
# Distributed Data Parallelism in native torch
OMP_NUM_THREADS=1 torchrun --standalone --nnodes=1 --nproc_per_node=gpu dicee --dataset_dir "KGs/UMLS" --model Keci --eval_model "train_val_test" --trainer "torchDDP" --scoring_technique KvsAll

A KGE model model can also be trained in multi-node multi-gpu DDP setting.

torchrun --nnodes 2 --nproc_per_node=gpu  --node_rank 0 --rdzv_id 455 --rdzv_backend c10d --rdzv_endpoint=nebula  dicee --trainer "torchDDP" --dataset_dir "KGs/YAGO3-10"
torchrun --nnodes 2 --nproc_per_node=gpu  --node_rank 1 --rdzv_id 455 --rdzv_backend c10d --rdzv_endpoint=nebula  dicee --trainer "torchDDP" --dataset_dir "KGs/YAGO3-10"

On large knowledge graphs, this configurations should be used.

where the data is in the following form

$ head -3 KGs/UMLS/train.txt 
acquired_abnormality    location_of     experimental_model_of_disease
anatomical_abnormality  manifestation_of        physiologic_function
alga    isa     entity

$ head -3 KGs/YAGO3-10/valid.txt 
Mikheil_Khutsishvili    playsFor        FC_Merani_Tbilisi
Ebbw_Vale       isLocatedIn     Blaenau_Gwent
Valenciennes    isLocatedIn     Nord-Pas-de-Calais

By default, --backend "pandas" --separator "\s+" is used in pandas.read_csv(sep=args.separator) to obtain triples. You can choose a suitable backend for your knowledge graph --backend pandas | polars | rdflib . On large knowledge graphs n-triples, --backend "polars" --separator " " is a good option. Apart from n-triples or standard link prediction dataset formats, we support ["owl", "nt", "turtle", "rdf/xml", "n3"]*. On other RDF knowledge graphs, --backend "rdflib" can be used. Note that knowledge graphs must not contain blank nodes or literals. Moreover, a KGE model can be also trained by providing an endpoint of a triple store.

dicee --sparql_endpoint "http://localhost:3030/mutagenesis/" --model Keci

Scoring Techniques

We have implemented state-of-the-art scoring techniques to train a KGE model --scoring_technique 1vsAll | KvsAll | AllvsAll | KvsSample | NegSample .

dicee --dataset_dir "KGs/YAGO3-10" --model Keci --trainer "torchCPUTrainer" --scoring_technique "NegSample" --neg_ratio 10 --num_epochs 10 --batch_size 10_000 --num_core 0 --eval_model None
# Epoch:10: 100%|███████████| 10/10 [01:31<00:00,  9.11s/it, loss_step=0.09423, loss_epoch=0.07897]
# Training Runtime: 1.520 minutes.
dicee --dataset_dir "KGs/YAGO3-10" --model Keci --trainer "torchCPUTrainer" --scoring_technique "NegSample" --neg_ratio 10 --num_epochs 10 --batch_size 10_000 --num_core 10 --eval_model None
# Epoch:10: 100%|███████████| 10/10 [00:58<00:00,  5.80s/it, loss_step=0.11909, loss_epoch=0.07991]
# Training Runtime: 58.106 seconds.
dicee --dataset_dir "KGs/YAGO3-10" --model Keci --trainer "torchCPUTrainer" --scoring_technique "NegSample" --neg_ratio 10 --num_epochs 10 --batch_size 10_000 --num_core 20 --eval_model None
# Epoch:10: 100%|███████████| 10/10 [01:01<00:00,  6.16s/it, loss_step=0.10751, loss_epoch=0.06962]
# Training Runtime: 1.029 minutes.
dicee --dataset_dir "KGs/YAGO3-10" --model Keci --trainer "torchCPUTrainer" --scoring_technique "NegSample" --neg_ratio 10 --num_epochs 10 --batch_size 10_000 --num_core 50 --eval_model None
# Epoch:10: 100%|███████████| 10/10 [01:08<00:00,  6.83s/it, loss_step=0.05347, loss_epoch=0.07003]
# Training Runtime: 1.140 minutes.

Increasing the number of cores often (but not always) helps to decrease the runtimes on large knowledge graphs --num_core 4 --scoring_technique KvsSample | NegSample --neg_ratio 1

A KGE model can be also trained in a python script

from dicee.executer import Execute
from dicee.config import Namespace
args = Namespace()
args.model = 'Keci'
args.scoring_technique = "KvsAll"  # 1vsAll, or AllvsAll, or NegSample
args.dataset_dir = "KGs/UMLS"
args.path_to_store_single_run = "Keci_UMLS"
args.num_epochs = 100
args.embedding_dim = 32
args.batch_size = 1024
reports = Execute(args).start()
print(reports["Train"]["MRR"]) # => 0.9912
print(reports["Test"]["MRR"]) # => 0.8155
# See the Keci_UMLS folder embeddings and all other files

Continual Learning

Train a KGE model by providing the path of a single file and store all parameters under newly created directory called KeciFamilyRun.

dicee --path_single_kg "KGs/Family/family-benchmark_rich_background.owl" --model Keci --path_to_store_single_run KeciFamilyRun --backend rdflib --eval_model None

where the data is in the following form

$ head -3 KGs/Family/train.txt 
_:1 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2002/07/owl#Ontology> .
<http://www.benchmark.org/family#hasChild> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2002/07/owl#ObjectProperty> .
<http://www.benchmark.org/family#hasParent> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2002/07/owl#ObjectProperty> .

Continual Training: the training phase of a pretrained model can be resumed. The model will saved in the same directory --continual_learning "KeciFamilyRun".

dicee --continual_learning "KeciFamilyRun" --path_single_kg "KGs/Family/family-benchmark_rich_background.owl" --model Keci --backend rdflib --eval_model None

Creating an Embedding Vector Database

To see a code snippet

Learning Embeddings

# Train an embedding model
dicee --dataset_dir KGs/Countries-S1 --path_to_store_single_run CountryEmbeddings --model Keci --p 0 --q 1 --embedding_dim 32 --adaptive_swa

Loading Embeddings into Qdrant Vector Database

# Ensure that Qdrant available
# docker pull qdrant/qdrant && docker run -p 6333:6333 -p 6334:6334      -v $(pwd)/qdrant_storage:/qdrant/storage:z      qdrant/qdrant
diceeindex --path_model "CountryEmbeddings" --collection_name "dummy" --location "localhost"

Launching Webservice

diceeserve --path_model "CountryEmbeddings" --collection_name "dummy" --collection_location "localhost"

Retrieve and Search

Get embedding of germany

curl -X 'GET' 'http://0.0.0.0:8000/api/get?q=germany' -H 'accept: application/json'

Get most similar things to europe

curl -X 'GET' 'http://0.0.0.0:8000/api/search?q=europe' -H 'accept: application/json'
{"result":[{"hit":"europe","score":1.0},
{"hit":"northern_europe","score":0.67126536},
{"hit":"western_europe","score":0.6010134},
{"hit":"puerto_rico","score":0.5051694},
{"hit":"southern_europe","score":0.4829831}]}

Answering Complex Queries

To see a code snippet

# pip install dicee
# wget https://files.dice-research.org/datasets/dice-embeddings/KGs.zip --no-check-certificate & unzip KGs.zip
from dicee.executer import Execute
from dicee.config import Namespace
from dicee.knowledge_graph_embeddings import KGE
# (1) Train a KGE model
args = Namespace()
args.model = 'Keci'
args.p=0
args.q=1
args.optim = 'Adam'
args.scoring_technique = "AllvsAll"
args.path_single_kg = "KGs/Family/family-benchmark_rich_background.owl"
args.backend = "rdflib"
args.num_epochs = 200
args.batch_size = 1024
args.lr = 0.1
args.embedding_dim = 512
result = Execute(args).start()
# (2) Load the pre-trained model
pre_trained_kge = KGE(path=result['path_experiment_folder'])
# (3) Single-hop query answering
# Query: ?E : \exist E.hasSibling(E, F9M167)
# Question: Who are the siblings of F9M167?
# Answer: [F9M157, F9F141], as (F9M167, hasSibling, F9M157) and (F9M167, hasSibling, F9F141)
predictions = pre_trained_kge.answer_multi_hop_query(query_type="1p",
                                                     query=('http://www.benchmark.org/family#F9M167',
                                                            ('http://www.benchmark.org/family#hasSibling',)),
                                                     tnorm="min", k=3)
top_entities = [topk_entity for topk_entity, query_score in predictions]
assert "http://www.benchmark.org/family#F9F141" in top_entities
assert "http://www.benchmark.org/family#F9M157" in top_entities
# (2) Two-hop query answering
# Query: ?D : \exist E.Married(D, E) \land hasSibling(E, F9M167)
# Question: To whom a sibling of F9M167 is married to?
# Answer: [F9F158, F9M142] as (F9M157 #married F9F158) and (F9F141 #married F9M142)
predictions = pre_trained_kge.answer_multi_hop_query(query_type="2p",
                                                     query=("http://www.benchmark.org/family#F9M167",
                                                            ("http://www.benchmark.org/family#hasSibling",
                                                             "http://www.benchmark.org/family#married")),
                                                     tnorm="min", k=3)
top_entities = [topk_entity for topk_entity, query_score in predictions]
assert "http://www.benchmark.org/family#F9M142" in top_entities
assert "http://www.benchmark.org/family#F9F158" in top_entities
# (3) Three-hop query answering
# Query: ?T : \exist D.type(D,T) \land Married(D,E) \land hasSibling(E, F9M167)
# Question: What are the type of people who are married to a sibling of F9M167?
# (3) Answer: [Person, Male, Father] since  F9M157 is [Brother Father Grandfather Male] and F9M142 is [Male Grandfather Father]

predictions = pre_trained_kge.answer_multi_hop_query(query_type="3p", query=("http://www.benchmark.org/family#F9M167",
                                                                             ("http://www.benchmark.org/family#hasSibling",
                                                                             "http://www.benchmark.org/family#married",
                                                                             "http://www.w3.org/1999/02/22-rdf-syntax-ns#type")),
                                                     tnorm="min", k=5)
top_entities = [topk_entity for topk_entity, query_score in predictions]
print(top_entities)
assert "http://www.benchmark.org/family#Person" in top_entities
assert "http://www.benchmark.org/family#Father" in top_entities
assert "http://www.benchmark.org/family#Male" in top_entities

For more, please refer to examples/multi_hop_query_answering.

Predicting Missing Links

To see a code snippet

from dicee import KGE
# (1) Train a knowledge graph embedding model..
# (2) Load a pretrained model
pre_trained_kge = KGE(path='..')
# (3) Predict missing links through head entity rankings
pre_trained_kge.predict_topk(h=[".."],r=[".."],topk=10)
# (4) Predict missing links through relation rankings
pre_trained_kge.predict_topk(h=[".."],t=[".."],topk=10)
# (5) Predict missing links through tail entity rankings
pre_trained_kge.predict_topk(r=[".."],t=[".."],topk=10)

Downloading Pretrained Models

We provide plenty pretrained knowledge graph embedding models at dice-research.org/projects/DiceEmbeddings/.

To see a code snippet

from dicee import KGE
mure = KGE(url="https://files.dice-research.org/projects/DiceEmbeddings/YAGO3-10-Pykeen_MuRE-dim128-epoch256-KvsAll")
quate = KGE(url="https://files.dice-research.org/projects/DiceEmbeddings/YAGO3-10-Pykeen_QuatE-dim128-epoch256-KvsAll")
keci = KGE(url="https://files.dice-research.org/projects/DiceEmbeddings/YAGO3-10-Keci-dim128-epoch256-KvsAll")
quate.predict_topk(h=["Mongolia"],r=["isLocatedIn"],topk=3)
# [('Asia', 0.9894362688064575), ('Europe', 0.01575559377670288), ('Tadanari_Lee', 0.012544365599751472)]
keci.predict_topk(h=["Mongolia"],r=["isLocatedIn"],topk=3)
# [('Asia', 0.6522021293640137), ('Chinggis_Khaan_International_Airport', 0.36563414335250854), ('Democratic_Party_(Mongolia)', 0.19600993394851685)]
mure.predict_topk(h=["Mongolia"],r=["isLocatedIn"],topk=3)
# [('Asia', 0.9996906518936157), ('Ulan_Bator', 0.0009907372295856476), ('Philippines', 0.0003116439620498568)]

How to Deploy

To see a single line of code

from dicee import KGE
KGE(path='...').deploy(share=True,top_k=10)

To see the interface of the webservice

Link Prediction Benchmarks

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

To see the results

		MRR	Hits@1	Hits@3	Hits@10
ComplEx-KvsAll	train	1.000	1.000	1.000	1.000
ComplEx-KvsAll	val	0.374	0.308	0.402	0.501
ComplEx-KvsAll	test	0.372	0.302	0.404	0.505
ComplEx-KvsAll-SWA	train	0.998	0.997	1.000	1.000
ComplEx-KvsAll-SWA	val	0.345	0.279	0.372	0.474
ComplEx-KvsAll-SWA	test	0.341	0.272	0.374	0.474
ComplEx-KvsAll-SWA	train	x	x	x	x
ComplEx-KvsAll-SWA	val	x	x	x	x
ComplEx-KvsAll-SWA	test	x	x	x	x
Keci-KvsAll	train	1.000	1.000	1.000	1.000
Keci-KvsAll	val	0.337	0.268	0.370	0.468
Keci-KvsAll	test	0.343	0.274	0.376	0.343
Keci-KvsAll-SWA	train	1.000	1.000	1.000	1.000
Keci-KvsAll-SWA	val	0.325	0.253	0.358	0.459
Keci-KvsAll-SWA	test	0.334	0.263	0.367	0.470
Keci-KvsAll-ASWA	train	0.978	0.969	0.985	0.991
Keci-KvsAll-ASWA	val	0.400	0.324	0.439	0.540
Keci-KvsAll-ASWA	test	0.394	0.317	0.439	0.539

--embedding_dim 256 --num_epochs 300 --batch_size 1024 --optim Adam 0.1 leading to 31.6M params. Observations: A severe overfitting. ASWA improves the generalization better than SWA.

FB15k-237

		MRR	Hits@1	Hits@3	Hits@10
Keci-KvsAll-SWA	train	x	x	x	x
Keci-KvsAll-SWA	val	x	x	x	x
Keci-KvsAll-SWA	test	x	x	x	x

WN18RR

To see the results

UMLS

To see the results

		MRR	Hits@1	Hits@3	Hits@10
ComplEx-KvsAll	train	1.000	1.000	1.000	1.000
ComplEx-KvsAll	val	0.684	0.557	0.771	0.928
ComplEx-KvsAll	test	0.680	0.563	0.750	0.918
ComplEx-AllvsAll	train	1.000	1.000	1.000	1.000
ComplEx-AllvsAll	val	0.771	0.670	0.847	0.949
ComplEx-AllvsAll	test	0.778	0.678	0.850	0.957
ComplEx-KvsAll-SWA	train	1.000	1.000	1.000	1.000
ComplEx-KvsAll-SWA	val	0.762	0.666	0.825	0.941
ComplEx-KvsAll-SWA	test	0.757	0.653	0.833	0.939
ComplEx-AllvsAll-SWA	train	1.000	1.000	1.000	1.000
ComplEx-AllvsAll-SWA	val	0.817	0.736	0.879	0.953
ComplEx-AllvsAll-SWA	test	0.827	0.748	0.883	0.967
ComplEx-KvsAll-ASWA	train	0.998	0.997	0.999	1.000
ComplEx-KvsAll-ASWA	val	0.799	0.712	0.863	0.946
ComplEx-KvsAll-ASWA	test	0.804	0.720	0.866	0.948
ComplEx-AllvsAll-ASWA	train	0.998	0.997	0.998	0.999
ComplEx-AllvsAll-ASWA	val	0.879	0.824	0.926	0.964
ComplEx-AllvsAll-ASWA	test	0.877	0.819	0.924	0.971
Keci-KvsAll	train	1.000	1.000	1.000	1.000
Keci-KvsAll	val	0.538	0.401	0.595	0.829
Keci-KvsAll	test	0.543	0.411	0.610	0.815
Keci-AllvsAll	train	1.000	1.000	1.000	1.000
Keci-AllvsAll	val	0.672	0.556	0.742	0.909
Keci-AllvsAll	test	0.684	0.567	0.759	0.914
Keci-KvsAll-SWA	train	1.000	1.000	1.000	1.000
Keci-KvsAll-SWA	val	0.633	0.509	0.705	0.877
Keci-KvsAll-SWA	test	0.628	0.498	0.710	0.868
Keci-AllvsAll-SWA	train	1.000	1.000	1.000	1.000
Keci-AllvsAll-SWA	val	0.697	0.584	0.770	0.911
Keci-AllvsAll-SWA	test	0.711	0.606	0.775	0.921
Keci-KvsAll-ASWA	train	0.996	0.993	0.999	1.000
Keci-KvsAll-ASWA	val	0.767	0.668	0.836	0.944
Keci-KvsAll-ASWA	test	0.762	0.660	0.830	0.949
Keci-AllvsAll-ASWA	train	0.998	0.997	0.999	1.000
Keci-AllvsAll-ASWA	val	0.852	0.793	0.896	0.955
Keci-AllvsAll-ASWA	test	0.848	0.787	0.886	0.951

--embedding_dim 256 --num_epochs 300 --batch_size 1024 --optim Adam 0.1 leading to 58.1K params. Observations:

A severe overfitting.
AllvsAll improves the generalization more than KvsAll does
ASWA improves the generalization more than SWA does

dicee --dataset_dir "KGs/UMLS" --model "Keci" --p 0 --q 1 --trainer "PL" --scoring_technique "KvsSample" --embedding_dim 256 --num_epochs 100 --batch_size 32 --num_core 10
# Epoch 99: 100%|███████████| 13/13 [00:00<00:00, 29.56it/s, loss_step=6.46e-6, loss_epoch=8.35e-6]
# *** Save Trained Model ***
# Evaluate Keci on Train set: Evaluate Keci on Train set
# {'H@1': 1.0, 'H@3': 1.0, 'H@10': 1.0, 'MRR': 1.0}
# Evaluate Keci on Validation set: Evaluate Keci on Validation set
# {'H@1': 0.33358895705521474, 'H@3': 0.5253067484662577, 'H@10': 0.7576687116564417, 'MRR': 0.46992150194876076}
# Evaluate Keci on Test set: Evaluate Keci on Test set
# {'H@1': 0.3320726172465961, 'H@3': 0.5098335854765507, 'H@10': 0.7594553706505295, 'MRR': 0.4633434701052234}

Increasing cores increases the runtimes if there is a preprocessing step at the batch generation.

dicee --dataset_dir "KGs/UMLS" --model "Keci" --p 0 --q 1 --trainer "PL" --scoring_technique "KvsAll" --embedding_dim 256 --num_epochs 100 --batch_size 32
# Epoch 99: 100%|██████████| 13/13 [00:00<00:00, 101.94it/s, loss_step=8.11e-6, loss_epoch=8.92e-6]
# Evaluate Keci on Train set: Evaluate Keci on Train set
# {'H@1': 1.0, 'H@3': 1.0, 'H@10': 1.0, 'MRR': 1.0}
# Evaluate Keci on Validation set: Evaluate Keci on Validation set
# {'H@1': 0.348159509202454, 'H@3': 0.5659509202453987, 'H@10': 0.7883435582822086, 'MRR': 0.4912162082105331}
# Evaluate Keci on Test set: Evaluate Keci on Test set
# {'H@1': 0.34568835098335854, 'H@3': 0.5544629349470499, 'H@10': 0.7776096822995462, 'MRR': 0.48692617590763265}

dicee --dataset_dir "KGs/UMLS" --model "Keci" --p 0 --q 1 --trainer "PL" --scoring_technique "AllvsAll" --embedding_dim 256 --num_epochs 100 --batch_size 32
# Epoch 99: 100%|██████████████| 98/98 [00:01<00:00, 88.95it/s, loss_step=0.000, loss_epoch=0.0655]
# Evaluate Keci on Train set: Evaluate Keci on Train set
# {'H@1': 0.9976993865030674, 'H@3': 0.9997124233128835, 'H@10': 0.9999041411042945, 'MRR': 0.9987183437408705}
# Evaluate Keci on Validation set: Evaluate Keci on Validation set
# {'H@1': 0.3197852760736196, 'H@3': 0.5398773006134969, 'H@10': 0.7714723926380368, 'MRR': 0.46912531544840963}
# Evaluate Keci on Test set: Evaluate Keci on Test set
# {'H@1': 0.329803328290469, 'H@3': 0.5711043872919819, 'H@10': 0.7934947049924357, 'MRR': 0.4858500337837166}

In KvsAll and AllvsAll, a single data point z=(x,y) corresponds to a tuple of input indices x and multi-label output vector y. x is a tuple of indices of a unique entity and relation pair. y contains a binary vector of size of the number of unique entities.

To mitigate the rate of overfitting, many regularization techniques can be applied ,e.g., Stochastic Weight Averaging (SWA), Adaptive Stochastic Weight Averaging (ASWA), or Dropout. Use --swa to apply Stochastic Weight Averaging

dicee --dataset_dir "KGs/UMLS" --model "Keci" --p 0 --q 1 --trainer "PL" --scoring_technique "KvsAll" --embedding_dim 256 --num_epochs 100 --batch_size 32 --swa
# Epoch 99: 100%|███████████| 13/13 [00:00<00:00, 85.61it/s, loss_step=8.11e-6, loss_epoch=8.92e-6]
# Evaluate Keci on Train set: Evaluate Keci on Train set
# {'H@1': 1.0, 'H@3': 1.0, 'H@10': 1.0, 'MRR': 1.0}
# Evaluate Keci on Validation set: Evaluate Keci on Validation set
# {'H@1': 0.45858895705521474, 'H@3': 0.6510736196319018, 'H@10': 0.8458588957055214, 'MRR': 0.5845156794070833}
# Evaluate Keci on Test set: Evaluate Keci on Test set
# {'H@1': 0.4636913767019667, 'H@3': 0.651285930408472, 'H@10': 0.8456883509833586, 'MRR': 0.5877221440365971}
# Total Runtime: 25.417 seconds

Use --adaptive_swa to apply Adaptive Stochastic Weight Averaging. Currently, ASWA should not be used with DDP on multi GPUs. We are working on it.

CUDA_VISIBLE_DEVICES=0 dicee --dataset_dir "KGs/UMLS" --model "Keci" --p 0 --q 1 --trainer "PL" --scoring_technique "KvsAll" --embedding_dim 256 --num_epochs 100 --batch_size 32 --adaptive_swa
# Epoch 99: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 49/49 [00:00<00:00, 93.86it/s, loss_step=0.0978, loss_epoch=0.143]
# Evaluate Keci on Train set: Evaluate Keci on Train set
# {'H@1': 0.9974118098159509, 'H@3': 0.9992331288343558, 'H@10': 0.9996165644171779, 'MRR': 0.9983922084274367}
# Evaluate Keci on Validation set: Evaluate Keci on Validation set
# {'H@1': 0.7668711656441718, 'H@3': 0.8696319018404908, 'H@10': 0.9440184049079755, 'MRR': 0.828767705987023}
# Evaluate Keci on Test set: Evaluate Keci on Test set
#{'H@1': 0.7844175491679274, 'H@3': 0.8888048411497731, 'H@10': 0.9546142208774584, 'MRR': 0.8460991515345323}

CUDA_VISIBLE_DEVICES=0 dicee --dataset_dir "KGs/UMLS" --model "Keci" --p 0 --q 1 --trainer "PL" --scoring_technique "KvsAll" --embedding_dim 256 --input_dropout_rate 0.1 --num_epochs 100 --batch_size 32 --adaptive_swa
# Epoch 99: 100%|██████████████████████████████████████████████████████████| 49/49 [00:00<00:00, 93.49it/s, loss_step=0.600, loss_epoch=0.553]
# Evaluate Keci on Train set: Evaluate Keci on Train set
# {'H@1': 0.9970283742331288, 'H@3': 0.9992331288343558, 'H@10': 0.999808282208589, 'MRR': 0.9981489117237927}
# Evaluate Keci on Validation set: Evaluate Keci on Validation set
# {'H@1': 0.8473926380368099, 'H@3': 0.9049079754601227, 'H@10': 0.9470858895705522, 'MRR': 0.8839172788777631}
# Evaluate Keci on Test set: Evaluate Keci on Test set
# {'H@1': 0.8381240544629349, 'H@3': 0.9167927382753404, 'H@10': 0.9568835098335855, 'MRR': 0.8829572716873321}

CUDA_VISIBLE_DEVICES=0 dicee --dataset_dir "KGs/UMLS" --model "Keci" --p 0 --q 1 --trainer "PL" --scoring_technique "KvsAll" --embedding_dim 256 --input_dropout_rate 0.2 --num_epochs 100 --batch_size 32 --adaptive_swa
# Epoch 99: 100%|██████████████████████████████████████████████████████████| 49/49 [00:00<00:00, 94.43it/s, loss_step=0.108, loss_epoch=0.111]
# Evaluate Keci on Train set: Evaluate Keci on Train set
# {'H@1': 0.9818826687116564, 'H@3': 0.9942484662576687, 'H@10': 0.9972200920245399, 'MRR': 0.9885307022708297}
# Evaluate Keci on Validation set: Evaluate Keci on Validation set
# {'H@1': 0.8581288343558282, 'H@3': 0.9156441717791411, 'H@10': 0.9447852760736196, 'MRR': 0.8930935122236525}
# Evaluate Keci on Test set: Evaluate Keci on Test set
# {'H@1': 0.8494704992435703, 'H@3': 0.9334341906202723, 'H@10': 0.9667170953101362, 'MRR': 0.8959156201718665}

Docker

Details

To build the Docker image: ``` docker build -t dice-embeddings . ```

To test the Docker image:

docker run --rm -v ~/.local/share/dicee/KGs:/dicee/KGs dice-embeddings ./main.py --model AConEx --embedding_dim 16

How to cite

Currently, we are working on our manuscript describing our framework. If you really like our work and want to cite it now, feel free to chose one :)

# Keci
@inproceedings{demir2023clifford,
  title={Clifford Embeddings--A Generalized Approach for Embedding in Normed Algebras},
  author={Demir, Caglar and Ngonga Ngomo, Axel-Cyrille},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={567--582},
  year={2023},
  organization={Springer}
}
# LitCQD
@inproceedings{demir2023litcqd,
  title={LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals},
  author={Demir, Caglar and Wiebesiek, Michel and Lu, Renzhong and Ngonga Ngomo, Axel-Cyrille and Heindorf, Stefan},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={617--633},
  year={2023},
  organization={Springer}
}
# DICE Embedding Framework
@article{demir2022hardware,
  title={Hardware-agnostic computation for large-scale knowledge graph embeddings},
  author={Demir, Caglar and Ngomo, Axel-Cyrille Ngonga},
  journal={Software Impacts},
  year={2022},
  publisher={Elsevier}
}
# KronE
@inproceedings{demir2022kronecker,
  title={Kronecker decomposition for knowledge graph embeddings},
  author={Demir, Caglar and Lienen, Julian and Ngonga Ngomo, Axel-Cyrille},
  booktitle={Proceedings of the 33rd ACM Conference on Hypertext and Social Media},
  pages={1--10},
  year={2022}
}
# QMult, OMult, ConvQ, ConvO
@InProceedings{pmlr-v157-demir21a,
  title = 	 {Convolutional Hypercomplex Embeddings for Link Prediction},
  author =       {Demir, Caglar and Moussallem, Diego and Heindorf, Stefan and Ngonga Ngomo, Axel-Cyrille},
  booktitle = 	 {Proceedings of The 13th Asian Conference on Machine Learning},
  pages = 	 {656--671},
  year = 	 {2021},
  editor = 	 {Balasubramanian, Vineeth N. and Tsang, Ivor},
  volume = 	 {157},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--19 Nov},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v157/demir21a/demir21a.pdf},
  url = 	 {https://proceedings.mlr.press/v157/demir21a.html},
}
# ConEx
@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}}
# Shallom
@inproceedings{demir2021shallow,
  title={A shallow neural model for relation prediction},
  author={Demir, Caglar and Moussallem, Diego and Ngomo, Axel-Cyrille Ngonga},
  booktitle={2021 IEEE 15th International Conference on Semantic Computing (ICSC)},
  pages={179--182},
  year={2021},
  organization={IEEE}

For any questions or wishes, please contact: caglar.demir@upb.de

Name		Name	Last commit message	Last commit date
Latest commit History 1,479 Commits
.github/workflows		.github/workflows
dicee		dicee
docs		docs
examples		examples
tests		tests
.coveragerc		.coveragerc
Dockerfile		Dockerfile
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
requirements.txt		requirements.txt
setup.py		setup.py

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DICE Embeddings: Hardware-agnostic Framework for Large-scale Knowledge Graph Embeddings

For more please visit dice-embeddings!

Installation

Installation from Source

Download Knowledge Graphs

Knowledge Graph Embedding Models

How to Train

Training Techniques

Scoring Techniques

Continual Learning

Creating an Embedding Vector Database

Learning Embeddings

Loading Embeddings into Qdrant Vector Database

Launching Webservice

Retrieve and Search

Answering Complex Queries

Predicting Missing Links

Downloading Pretrained Models

How to Deploy

Link Prediction Benchmarks

YAGO3-10

FB15k-237

WN18RR

UMLS

Docker

How to cite

About

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dice-group/dice-embeddings

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Repository files navigation

DICE Embeddings: Hardware-agnostic Framework for Large-scale Knowledge Graph Embeddings

For more please visit dice-embeddings!

Installation

Installation from Source

Download Knowledge Graphs

Knowledge Graph Embedding Models

How to Train

Training Techniques

Scoring Techniques

Continual Learning

Creating an Embedding Vector Database

Learning Embeddings

Loading Embeddings into Qdrant Vector Database

Launching Webservice

Retrieve and Search

Answering Complex Queries

Predicting Missing Links

Downloading Pretrained Models

How to Deploy

Link Prediction Benchmarks

YAGO3-10

FB15k-237

WN18RR

UMLS

Docker

How to cite

About

Resources

License

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Releases 14

Packages 0

Contributors 10

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