Special issue on Knowledge Graph Construction

Call for papers: Special Issue on

Knowledge Graph Construction

This special issue aims to explore the latest research trends and enhance the state of the art in the field of knowledge graph construction from semi-structured data. While many methods and systems were proposed to construct knowledge graphs from existing semi-structured data stored in databases or available on the Web, knowledge graph construction is a complex task and there are still many open research questions.

Knowledge graphs (KGs) are key to the adoption of the Semantic Web. This special issue should provide a comprehensive overview of the latest research trend and inspire further research in this exciting and rapidly evolving area. We welcome original research papers that propose novel techniques, models, and frameworks for generating high-quality and reliable knowledge graphs from structured and semi-structured data, such as relational databases, CSV, XML, JSON etc.

Meeting the challenges associated with constructing knowledge graphs necessitates the investigation of theoretical concepts and the design of efficient systems and methodologies. Many declarative approaches were proposed, yet they lack corresponding formalizations. Although these declarative methods initially sparked the design of virtualisation systems,they did not gain as much traction as well in the case of heterogeneous data where a multitude of materialisation systems emerged to efficiently construct knowledge graphs. Even among materialisation systems, there is still ample space for enhancements and improvements but systematic evaluation methods were not proposed yet.

Knowledge graph construction was seen as the responsibility of a single entity (typically the data owner). However, with the advent of data spaces, knowledge graph construction has evolved entailing a whole new range of challenges, such as collaborative knowledge graph construction or conformance to a commonly agreed model. While early approaches have predominantly focused on declarative methods and systems to support users with the declaration of rules for knowledge graph construction, there has been lately a growing interest in leveraging large language models (LLMs) and machine learning (ML) algorithms in general in the field, opening up new research avenues for constructing knowledge graphs. Naturally, when machine learning comes into play, additional considerations, such as explainability, reproducibility and replicability become relevant.

Contributions along the aforementioned (but not limited to) directions are considered for the proposed special issue on knowledge graph construction, including theoretical investigations, lessons learned, and observations from in-use experiences.

Themes and Topics

  • Automated KG construction
    • (Semi-)automation KG construction
    • Machine Learning algorithms for KG construction, repair and refinement,
      e.g., embeddings or Large Language Models (LLM) for KG construction
    • (Symbolic and subsymbolic) Learning for KG construction
  • Mapping languages
    • Declarative approaches to KG construction
    • Mapping languages for KG construction
    • Formalizations of KG construction
    • RDF and property graphs construction
  • Governance
    • Data governance and KG Lifecycle management
    • End-to-end architectures and workflows for KG construction
    • Knowledge graph construction within Data Spaces, e.g., IDS, GAIA-X, Solid, etc.
  • Quality
    • Explainable KG construction
    • Quality methods and systems for KG construction
    • (Semi-)automated validation and repair for KG construction
  • Virtualization
    • Virtualization over heterogeneous data sources
    • Federation over heterogeneous data sources
    • Hybrid materialization and virtualization over heterogeneous data
  • Benchmarks
    • Lessons learnt, in-use, and experiences on construction KG in real environments
    • Benchmarks for KG construction for efficiency, scalability, etc.
  • Human Interaction
    • User interfaces for KG construction
    • Human-in-the-loop KG construction

Relevant Types of Submissions

The following are relevant types of submissions:

  • Research articles related to the relevant topics (full paper)
  • Surveys on topics related to KG construction (full paper)
  • Ontologies for KG construction (short ontology paper of 10 pages)
  • Application reports with deployed applications for KG construction (short
    papers of 10 pages)
  • Systems, UIs or benchmarks for KG construction (short system papers of 10


  • Submission deadline: 1st July 2024. Papers submitted before the deadline will be reviewed upon receipt.

Guest Editors

The guest editors can be reached at kgc_swj@grupos.nube.usc.gal.

David Chaves-Fraga, University of Santiago de Compostela, Spain & KULeuven, Belgium
Christophe Debruyne, University of Liège, Belgium
Anastasia Dimou, KU Leuven, Belgium
Maria-Esther Vidal, Leibniz University of Hannover, and TIB-Leibniz Information Centre for
Science and Technology, Germany

Guest Editorial Board

Marcelo Arenas, Pontificia Universidad Catolica de Chile, CL
Diego Calvanese, University of Bolzano & Umea University & Ontopic, IT
Oscar Corcho, Universidad Politécnica de Madrid, SP
Enrico Daga, The Open University, UK
Vasilis Efthymiou, FORTH, GR
Paul Groth, University of Amsterdam, NL
Olaf Hartig, Linköping University, SE
Aidan Hogan, Universidad de Chile, CL
Ernesto Jiménez-Ruiz, University of London, UK
Jakub Klímek, Charles University, CZ
Giorgos Konstantinidis, University of Southampton, UK
Manolis Koubarakis, National & Kapodistrian University of Athens, GR
Jose Emilio Labra Gayo, University of Oviedo, SP
David Lanti, Bolzano University, IT
Maxime Lefrancois, École des Mines de Saint-Étienne, FR
Franck Michel, Université Côte d'Azur, FR
Heiko Paulheim, University of Mannheim, DE
María Poveda, Universidad Politécnica de Madrid, SP
Anisa Rula, University of Brescia, IT
Umutcan Şimşek, University of Innsbruck, AT
Antoine Zimmermann, École des Mines de Saint-Étienne, FR
Filip Ilievski, University of Southern California, USA
Declan O’Sullivan, Trinity College Dublin, IE
Souripriya Das, Oracle, US
Juan Sequeda, data.world, US
Jesús Barrasa, Neo4J, US
Pedro, Amazon, US
Samaneh Jozashoori, metaphacts, DE
Vladimir Alexiev, Ontotext, BG
Ademar Crotti Junio, metaphacts, DE
Nuno Lopes, TopQuadrant, PT
Irene Celino, CEFRIEL, IT