PyTorch implementation of paper Knowledge Graph Representation Learning via Generated Descriptions([NLDB2023]) accepted by NLDB 2023.
Knowledge graph representation learning (KGRL) aims to project the entities and relations into a continuous low-dimensional knowledge graph space to be used for knowledge graph completion and detecting new triples. Using textual descriptions for entity representation learning has been a key topic. However, the current work has two major constraints: (1) some entities do not have any associated descriptions; (2) the associated descriptions are usually phrases, and they do not contain enough information. This paper presents a novel KGRL method for learning effective embeddings by generating meaningful descriptive sentences from entities’ connections. The experiments using four public datasets and a new proposed dataset show that the New Description-Embodied Knowledge Graph Embedding (NDKGE for short) approach introduced in this paper outperforms most of the existing work in the task of link prediction.
# install virtual environment module
python3 -m pip install --user virtualenv
# create virtual environment
python3 -m venv env_name
source env_name/bin/activate
# install python packages
pip install torch
pip install openke
python3 train_transe_ndkge.py --dataset_path ./benchmarks/WN18RR/ --dataset WN18RR --setting name --id_dim 20 --word_dim 50 --nbatches 100 --margin 2 --num_epochs 20000 --learning_rate 0.5 --model_name wn18rr_name_epochs-20000_margin-2_lr-1_id_dim-20_word_dim-50
--dataset_path: the path of the dataset.
--dataset: dataset name.
--setting: using different settings, such as 'name','mention',and 'description'
--id_dim: the dimension of id.
--word_dim: the dimension of the token.
--nbatches: batch size for training.
--learning_rate: learning rate for training.
--num_epochs: number of epochs for training.
--model_name: model name for output.
--margin: margin for loss function.
Download data from here
This paper has been accepted by the 28th International Conference on Natural Language & Information Systems (NLDB 2023). The published version can be viewed by this link. If you use any code from our repo in your paper, pls cite:
@InProceedings{mhu23_ndkge,
author="Hu, Miao
and Lin, Zhiwei
and Marshall, Adele",
editor="M{\'e}tais, Elisabeth
and Meziane, Farid
and Sugumaran, Vijayan
and Manning, Warren
and Reiff-Marganiec, Stephan",
title="Knowledge Graph Representation Learning via Generated Descriptions",
booktitle="Natural Language Processing and Information Systems",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="365--378"
}
Feel free to contact MiaoHu (mhu05@qub.ac.uk), if you have any further questions.