functionnal Embeddings: Embeddings Knowledge Graph in function space

Knowledge graph embedding has shown to be succesfull when using divisional algebras ($\mathbb{R}$, $\mathbb{C}$, $\mathbb{Q}$, etc..) as these space are useful to model complex relations and pattern in a KG dataset. So far only vectors have been used to computed the ebeddings of KGs. This repository aim to extend this idea and consider another alternative which is function space. So we compute represent the embeddings of entities and relations as functions. First with polynomial functions then trigonometric and neural network function. Here we implemented three functional embedding model. The first called PolyMult embed using polynomial functions. The second, called LFMult1 embed using trigonometric function and the third called LFMult embed using Neural Networks.

Installation

First, make sure you have anaconda installed

Click me!

Installation from Source

conda create -n decal python=3.10.13 --no-default-packages && conda activate decal && cd functionnal-embeddings &&
pip3 install -e .

Download Knowledge Graphs

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

Knowledge Graph Embedding Models

To see available Models

1. TransE, DistMult, ComplEx, ConEx, QMult, OMult, ConvO, ConvQ, PolyMult, LFMult, FMult, LFMult1

How to use this repo?

First install all the necessary packages using:

 pip install -r requirements.txt 

Embedding with polynomials:

To get the results obtained in the paper for the UMLS data, do:

python3 run.py --model PolyMult --eval_model "train_val_test" --scoring_technique NegSample --degree 1 --lr 0.02 --embedding_dim 32 --num_epochs 500 --neg_ratio 50 --optim Adam --batch_size 1024

Embedding with trigonometric function:

python3 run.py --model LFMult1 --eval_model "train_val_test" --scoring_technique NegSample --degree 1 --lr 0.02 --embedding_dim 32 --num_epochs 500 --neg_ratio 50 --optim Adam --batch_size 1024

Embedding with Neural Networks:

python3 run.py --model LFMult1 --eval_model "train_val_test" --scoring_technique NegSample --degree 1 --lr 0.02 --embedding_dim 32 --num_epochs 500 --neg_ratio 50 --optim Adam --batch_size 1024