AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

Abstract

In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the first time, the creation of both content-based features—i.e., features based on RDF datatype properties—and topology-based features—i.e., features based on RDF object properties. Characterized by automated feature extraction, AutoRDF2GML makes it possible even for users less familiar with RDF and SPARQL to generate data representations ready for graph machine learning tasks, such as link prediction, node classification, and graph classification. Furthermore, we present four new benchmark datasets for graph machine learning, created from large RDF knowledge graphs using our framework. These datasets serve as valuable resources for evaluating graph machine learning approaches, such as graph neural networks. Overall, our framework effectively bridges the gap between the Graph Machine Learning and Semantic Web communities, paving the way for RDF-based machine learning applications.

Details

OriginalspracheEnglisch
TitelThe Semantic Web – ISWC 2024 - 23rd International Semantic Web Conference, Proceedings
Redakteure/-innenGianluca Demartini, Katja Hose, Maribel Acosta, Matteo Palmonari, Gong Cheng, Hala Skaf-Molli, Nicolas Ferranti, Daniel Hernández, Aidan Hogan
Herausgeber (Verlag)Springer Science and Business Media B.V.
Seiten115-133
Seitenumfang19
ISBN (Print)9783031778469
PublikationsstatusVeröffentlicht - 27 Nov. 2024
Peer-Review-StatusJa

Publikationsreihe

ReiheLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band15233 LNCS
ISSN0302-9743

Konferenz

Titel23rd International Semantic Web Conference
KurztitelISWC 2024
Veranstaltungsnummer23
Dauer11 - 15 November 2024
Webseite
OrtLive! Casino & Hotel Maryland
StadtBaltimore
LandUSA/Vereinigte Staaten

Schlagworte

Forschungsprofillinien der TU Dresden