DLCC Gold Standard

Item Type:	Dataset
Title:	DLCC Gold Standard
Date:	2022
Creator:	Portisch, Jan ORCID: 0000-0001-5420-0663 ; Paulheim, Heiko ORCID: 0000-0003-4386-8195
Divisions:	School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)

DDC Classification:	004 Computer science, internet
Abstract:	Knowledge graph embedding is a representation learning technique which projects entities and relations in a knowledge graph to continuous vector spaces. Embeddings have gained a lot of uptake and have been heavily used in link prediction and other downstream prediction tasks. Most approaches are evaluated on a single task or a single group of tasks to determine their overall performance. The evaluation is then assessed in terms of how well the embedding approach performs on the task at hand, but it is hardly evaluated (and often not even deeply understood) what information the embedding approaches are actually learning to represent. To fill this gap, we present the DLCC (Description Logic Class Constructors) benchmark, a resource to analyze embedding approaches in terms of which kinds of classes they can represent. Two gold standards are presented, one based on the real world knowledge graph DBpedia, and one synthetic gold standard.
External Identifier for Data:	https://doi.org/10.5281/zenodo.6509715

URL:	https://madata.bib.uni-mannheim.de/568/
Access (Controlled):	Only Metadata
License (Controlled):	Creative Commons: CC-BY | Attribution 4.0 (recommended)

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Date Deposited:	30 Mar 2026 10:16
Last Modified:	30 Mar 2026 10:16