A closer look at sum-based embeddings for knowledge graphs containing procedural knowledge

While knowledge graphs and their embedding into low dimensional vectors are established fields of research, they mostly cover factual knowledge. However, to improve downstream models, e. g. for predictive quality in real-world industrial use cases, embeddings of procedural knowledge, available in the form of rules, could be utilized. As such, we investigate which properties of embedding algorithms could prove beneficial in this scenario and evaluate which established embedding methodologies are suited to form the basis of sum-based embeddings of different representations of procedural knowledge.

Metadaten
Author:	Richard Nordsieck, Michael Heider ORCiD GND, Anton Hummel, Jörg Hähner ORCiD GND
URN:	urn:nbn:de:bvb:384-opus4-1040097
Frontdoor URL	https://opus.bibliothek.uni-augsburg.de/opus4/104009
URL:	https://ceur-ws.org/Vol-3342/
ISSN:	1613-0073OPAC
Parent Title (English):	DL4KG 2022 - Deep Learning for Knowledge Graphs 2022: Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2022) co-located with the 21th International Semantic Web Conference (ISWC 2022), virtual conference, online, October 24, 2022
Publisher:	CEUR-WS
Place of publication:	Aachen
Editor:	Mehwish Alam, Davide Buscaldi, Michael Cochez, Francesco Osborne, Diego Reforgiato Recupero
Type:	Conference Proceeding
Language:	English
Year of first Publication:	2022
Publishing Institution:	Universität Augsburg
Release Date:	2023/04/25
First Page:	7
Series:	CEUR Workshop Proceedings ; 3342
Institutes:	Fakultät für Angewandte Informatik
	Fakultät für Angewandte Informatik / Institut für Informatik
	Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Dewey Decimal Classification:	0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):	CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)

Open Access