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How Human-Centric Are Our Graph Data Abstractions?

Authors Angela Bonifati , Anastasia Dimou , Stefania Dumbrava , George Fletcher , Katja Hose , George Konstantinidis , Jose Emilio Labra Gayo , Wim Martens , Nina Pardal , Liat Peterfreund , Katherine Thornton , Maria-Esther Vidal , Hannes Voigt

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Subject Classification

ACM Subject Classification
  • Information systems → Graph-based database models
  • Information systems → Resource Description Framework (RDF)
  • Human-centered computing → Human computer interaction (HCI)
  • Social and professional topics → Computing education
  • Information systems → Query languages
  • Computing methodologies → Knowledge representation and reasoning
Keywords
  • graph data abstractions
  • property graphs
  • RDF
  • human-centric data management
  • usability
  • computing education

Metrics

Abstract

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.

Cite As Get BibTex

Angela Bonifati, Anastasia Dimou, Stefania Dumbrava, George Fletcher, Katja Hose, George Konstantinidis, Jose Emilio Labra Gayo, Wim Martens, Nina Pardal, Liat Peterfreund, Katherine Thornton, Maria-Esther Vidal, and Hannes Voigt. How Human-Centric Are Our Graph Data Abstractions?. In Special Issue on Data Management for (Knowledge) Graphs. Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 2, pp. 2:1-2:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/TGDK.4.2.2

Author Details

Angela Bonifati
  • Lyon 1 University, CNRS LIRIS, France
  • IUF, Lyon, France
Anastasia Dimou
  • KU Leuven, Flanders Make@KULeuven, Leuven.AI, Belgium
Stefania Dumbrava
  • ENSIIE, SAMOVAR, Télécom SudParis, France
  • IRIF, INRIA Paris, France
George Fletcher
  • Eindhoven University of Technology, The Netherlands
Katja Hose
  • TU Wien, Austria
George Konstantinidis
  • University of Southampton, UK
Jose Emilio Labra Gayo
  • WESO Lab, University of Oviedo, Spain
Wim Martens
  • University of Bayreuth, Germany
Nina Pardal
  • University of Edinburgh, UK
Liat Peterfreund
  • Hebrew University of Jerusalem, Israel
Katherine Thornton
  • Yale University Library, New Haven, CT, USA
Maria-Esther Vidal
  • Data Science Institute, Leibniz University Hannover, Germany
  • TIB-Leibniz Institute for Science and Technology, Hannover, Germany
  • L3S-Research Center, Hannvcer, Germany
Hannes Voigt
  • Neo4j, Leipzig, Germany

Funding

  • Bonifati, Angela: Partially supported by the ANR Verigraph (ANR-21-CE48-0015) project.
  • Dumbrava, Stefania: Partially supported by the ANR JCJC VERDI (ANR-24-CE25-1109) project.
  • Hose, Katja: Partially supported by the Vienna Science and Technology Fund (WWTF) [DORSET, grant id: 10.47379/ICT25032].
  • Gayo, Jose Emilio Labra: Partially funded by projects SHAKIGRA: Shaping Knowledge and Interoperable Graphs, code: PID2024-157010OBI00, and project SEK25-GRU-GIC-24-089.
  • Pardal, Nina: Partially funded by DFG grant VI 1045-1/1, and Huawei Strategic Talent Program.
  • Peterfreund, Liat: Supported by Israel Science Foundation 2355/24.
  • Vidal, Maria-Esther: Partially funded by Leibniz Association, program "Leibniz Best Minds: Programme for Women Professors", project TrustKG-Transforming Data in Trustable Insights (Grant P99/2020), and by the Lower Saxony Ministry of Science and Culture (MWK) with funds from the Volkswagen Foundation’s zukunft.niedersachsen program (CAIMed - Lower Saxony Center for AI and Causal Methods in Medicine; GA No. ZN4257).

Acknowledgements

This work builds on discussions and outcomes from the Dagstuhl Seminar 24102 Shapes in Graph Data: Theory and Implementation [Shqiponja Ahmetaj et al., 2024].

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