Knowledge Graphs Alignment Based on Learning to Rank Methods

Victor Yamamoto, Julio Reis

2023

Abstract

Knowledge graphs (KGs) define facts expressed as triples in representing knowledge. Usually, several knowledge graphs are published in a given domain. It is relevant to create alignments both for classes that model concepts and between instances of those classes defined in different knowledge graphs. In this article, we study techniques for aligning entities expressed in KGs. Our solution explores the supervised ranking aggregation method in the alignment based on similarity values. Our experiments rely on the dataset from the Ontology Alignment Evaluation Initiative to evaluate the proposed method in experimental analyzes. Obtained results indicate the effectiveness in our alignment technique in the investigated datasets.

Paper Citation

in Harvard Style

Yamamoto V. and Reis J. (2023). Knowledge Graphs Alignment Based on Learning to Rank Methods. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-671-2, SciTePress, pages 315-322. DOI: 10.5220/0012258100003598

in Bibtex Style

@conference{keod23,
author={Victor Yamamoto and Julio Reis},
title={Knowledge Graphs Alignment Based on Learning to Rank Methods},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2023},
pages={315-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012258100003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Knowledge Graphs Alignment Based on Learning to Rank Methods
SN - 978-989-758-671-2
AU - Yamamoto V.
AU - Reis J.
PY - 2023
SP - 315
EP - 322
DO - 10.5220/0012258100003598
PB - SciTePress