Graph Hierarchy and Language Model-Based Explainable Entity Alignment of Knowledge Graphs

Kunyoung Kim, Donggyu Kim, Mye Sohn

2024

Abstract

This paper proposes a Graph hierarchy and Language model-based Explainable Entity Alignment (GLEE) framework to perform Entity Alignment (EA) between two or more Knowledge Graphs (KGs) required for solving complex problems. Unlike existing EA methods that generate embedding for entities using KG structure information to calculate the similarity between entities, the GLEE framework additionally utilizes graph hierarchy and datatype properties to find entities to be aligned. In the GLEE framework, the semantically similar hyper-entities of the entities to be aligned are discovered to reflect graph hierarchy in the alignment. Also, the semantically similar datatype properties and their values of the entities are also utilized in EA. At this time, language model is utilized to calculate semantic similarity of the hyper-entities or datatype properties. As a result, the GLEE framework can trustworthy explain why the two entities are aligned using the subgraphs that consist of similar hyper-entities, semantically identical properties, and their data values. To show the superiority of the GLEE framework, the experiment is performed using real world dataset to prove the EA performance and explainability.

Paper Citation

in Harvard Style

Kim K., Kim D. and Sohn M. (2024). Graph Hierarchy and Language Model-Based Explainable Entity Alignment of Knowledge Graphs. In Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS; ISBN 978-989-758-720-7, SciTePress, pages 124-131. DOI: 10.5220/0013012300003886

in Bibtex Style

@conference{explains24,
author={Kunyoung Kim and Donggyu Kim and Mye Sohn},
title={Graph Hierarchy and Language Model-Based Explainable Entity Alignment of Knowledge Graphs},
booktitle={Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS},
year={2024},
pages={124-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013012300003886},
isbn={978-989-758-720-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS
TI - Graph Hierarchy and Language Model-Based Explainable Entity Alignment of Knowledge Graphs
SN - 978-989-758-720-7
AU - Kim K.
AU - Kim D.
AU - Sohn M.
PY - 2024
SP - 124
EP - 131
DO - 10.5220/0013012300003886
PB - SciTePress