2026-09-04T07:11:33Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27583 2026-09-03T08:33:48Z ddc:004 open_access

How Human-Centric Are Our Graph Data Abstractions? Bonifati, Angela Dimou, Anastasia Dumbrava, Stefania Fletcher, George Hose, Katja Konstantinidis, George Gayo, Jose Emilio Labra Martens, Wim Pardal, Nina Peterfreund, Liat Thornton, Katherine Vidal, Maria-Esther Voigt, Hannes graph data abstractions property graphs RDF human-centric data management usability computing education Graph data abstractions are often assumed to be intuitive, but experience shows that they are not equally understandable or usable in practice. In this vision and challenges paper, we examine the human-centricity of contemporary graph data abstractions through four lenses: researchability, usability, teachability, and societal impact. Drawing on diverse real-world use cases, ranging from clinical data and collaborative knowledge bases to biological and pangenomic graphs, we distill insights from database research, human-computer interaction, and education. Based on this analysis, we identify open research challenges that must be addressed to make graph abstractions easier to study, use, learn, and reason about. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Angela Bonifati and Anastasia Dimou and Stefania Dumbrava and George Fletcher and Katja Hose and George Konstantinidis and Jose Emilio Labra Gayo and Wim Martens and Nina Pardal and Liat Peterfreund and Katherine Thornton and Maria-Esther Vidal and Hannes Voigt 2026 Is Part Of TGDK, Volume 4, Issue 2 (2024): Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge, Volume 4, Issue 2 Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/TGDK.4.2.2 urn:nbn:de:0030-drops-275837 https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.2.2 eng https://creativecommons.org/licenses/by/4.0/legalcode