Neuro-Symbolic Integration for Reasoning and Learning on Knowledge Graphs
Abstract
The goal of this thesis is to address knowledge graph completion tasks using neuro-symbolic methods. Neuro-symbolic methods allow the joint utilization of symbolic information defined as meta-rules in ontologies and knowledge graph embedding methods that represent entities and relations of the graph in a low-dimensional vector space. This approach has the potential to improve the resolution of knowledge graph completion tasks in terms of reliability, interpretability, data-efficiency and robustness.
Cite
Text
Werner. "Neuro-Symbolic Integration for Reasoning and Learning on Knowledge Graphs." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30415Markdown
[Werner. "Neuro-Symbolic Integration for Reasoning and Learning on Knowledge Graphs." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/werner2024aaai-neuro/) doi:10.1609/AAAI.V38I21.30415BibTeX
@inproceedings{werner2024aaai-neuro,
title = {{Neuro-Symbolic Integration for Reasoning and Learning on Knowledge Graphs}},
author = {Werner, Luisa},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23429-23430},
doi = {10.1609/AAAI.V38I21.30415},
url = {https://mlanthology.org/aaai/2024/werner2024aaai-neuro/}
}