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dicee 0.1.3.2
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@Demirrr Demirrr released this 19 Feb 07:44
· 250 commits to main since this release
v1.0.1.3.2
117cba5

We're happy to announce the 0.1.3.2 release. You can install it with pip install dicee

Some of Important New Features

  • Inductive Neural Link Prediction

  • AllvsAll Scoring Technique

  • Stochastic Weight Averaging and Adaptive Stochastic Weight Averaging

  • Qdrant Vector Database

Inductive Neural Link Prediction

We design a technique to transform most (if not all) transductive knowledge graph embeddings to inductive knowledge graph embedding models.
With our technique called (research paper is under review), a knowledge graph embedding model can operate on triples involving unseen entities and or relations.
By using byte_pair_encoding, a knowledge graph embedding model can compute an unnormalized log likelihood of a triple involving an unseen entity or unseen relation or a literal.

from dicee.executer import Execute
from dicee.config import Namespace
from dicee import KGE
args = Namespace()
args.dataset_dir = 'KGs/UMLS'
args.model = 'Keci'
args.byte_pair_encoding = True
result = Execute(args).start()
pre_trained_kge = KGE(path=result['path_experiment_folder'])
assert (pre_trained_kge.predict(h="alga", r="isa", t="entity", logits=True) >=pre_trained_kge.predict(h="Demir", r="loves", t="Embeddings", logits=True))

AllvsAll Scoring Technique

AllvsAll is a new scoring technique to train knowledge graph embedding models that is although computationaly more extensive (e.g. the size of the trainin data becomes the number of entities times the number of relations).
A knowledge graph embedding model can be trained with NegSample | 1vsAll | KvsAll | AllvsAll techniques

dicee --model Keci --scoring_technique AllvsAll 
dicee --model Pykeen_MuRE --scoring_technique AllvsAll

Stochastic Weight Averaging and Adaptive Stochastic Weight Averaging

Read Averaging Weights Leads to Wider Optima and Better Generalization
to know more about Stochastic Weight Averaging (SWA).

dicee --model ComplEx --swa

We design a technique called Adaptive SWA that combines SWA with early stopping technique.

dicee --model Keci --adaptive_swa

Qdrant Vector Database

Train a KGE model and store it into CountryEmbeddings.

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

Create a vector database

diceeindex --path_model "CountryEmbeddings" --collection_name "dummy" --location "localhost"

Launch Webservice

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

Query Vector Database

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