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https://ceur-ws.org/Vol-3718/preface.pdf
Preface for the 5th Edition of the International
Knowledge Graph Construction Workshop
David Chaves-Fraga1,2 , Anastasia Dimou2,3,4 , Ana Iglesias-Molina5 , Umutcan Serles6
and Dylan Van Assche7
1
Universidade de Santiago de Compostela, Departamento de Electrónica e Computación, Santiago de Compostela, Spain
2
KU Leuven, Department of Computer Science, Sint-Katelijne-Waver, Belgium
3
Flanders Make – DTAI-FET
4
Leuven.AI – KU Leuven institute for AI, B-3000 Leuven, Belgium
5
Universidad Politécnica de Madrid, Campus de Montegancedo, Boadilla del Monte, Spain
6
Semantic Technology Institute Innsbruck, Universität Innsbruck, Austria
7
IDLab, Dept of Electronics and Information Systems, Ghent University – imec, Belgium
More and more knowledge graphs are constructed for private use, e.g., the Amazon Prod-
uct Graph [1] or the Fashion Knowledge Graph by Zalando1 ,or public use, e.g., DBpedia2 or
Wikidata3 . While techniques to automatically construct KGs from existing Web objects exist
(e.g., scraping Web tables), there is still room for improvement. So far, constructing knowledge
graphs was considered an engineering task, however, more scientifically robust methods keep
on emerging. These methods were widely questioned for their verbosity, low performance
or difficulty of use, while the data sources’ variety and complexity cause further syntax and
semantic interoperability issues.
Declarative methods (mapping languages) for describing rules to construct knowledge graphs
and approaches to execute those rules keep on emerging. Nevertheless constructing knowledge
graphs is still not a straightforward task because several existing challenges remain and yet
the barriers to construct knowledge graphs are not lowered enough to be easily and broadly
adopted by industry. These reasons and the vastly populated knowledge graph construction W3C
Community Group4 show that there are still open questions that require further investigation
to come up with groundbreaking solutions.
Addressing challenges related to knowledge graphs construction requires well-founded
research, including the investigation of concepts and development of tools as well as methods
for their evaluation. R2RML was recommended in 2012 by W3C, and since then, different
extensions, alternatives and implementations were proposed [2, 3, 4]. Certain approaches
followed the ETL-like paradigm, e.g., SDM-RDFizer [5], RocketRML [6], and FunMap [7], while
Fifth International Workshop On Knowledge Graph Construction Co-located with the ESWC 2024, 27th May 2024, Crete,
Greece
Envelope-Open david.chaves@upm.es (D. Chaves-Fraga); anastasia.dimou@kuleuven.be (A. Dimou); ana.iglesiasm@upm.es
(A. Iglesias-Molina); umutcan.serles@sti2.at (U. Serles); dylan.van.assche@ugent.be (D. V. Assche)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
CEUR Workshop Proceedings (CEUR-WS.org)
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
https://engineering.zalando.com/posts/2018/03/semantic-web-technologies.html
2
https://www.dbpedia.org/resources/knowledge-graphs/
3
https://www.wikidata.org/wiki/Wikidata:Main_Page
4
http://w3.org/community/kg-construct
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
others the query-answering paradigm, e.g., Ultrawrap [8], Morph-RDB [9] and Ontop [10].
Besides R2RML-based extensions, alternatives were proposed, e.g., SPARQL-Generate [11] and
ShExML [12], as well as methods to perform data transformations while constructing knowledge
graphs, e.g., FnO [13] and FunUL [14].
The fifth edition of the knowledge graph construction workshop5 has a special focus on
time on novel techniques, frameworks, architectures, and tools for the new extensions of RML
such as RDF Collections and Containers, and RDF-Star support and the 2023 release of the RDF
Mapping Language (RML) [15] in general. It also included:
• Keynote. The workshop includes the keynote from Lionel Tailhardat (Orange): “Anomaly
Detection For Telco Companies: Challenges And Opportunities In Knowledge Graph
Construction”
• The Second Knowledge Graph Construction Challenge. The edition of this year’s challenge
has a double objective: benchmarking systems to (i) find which RDF graph construction
system optimizes for metrics i.e. execution time, CPU and memory usage; and (ii) how
compliant are they with the 2023 revision of RML and its new modules.
The final goal of the event is to provide a venue for scientific discourse, systematic analysis
and rigorous evaluation of languages, techniques and tools, as well as practical and applied
experiences and lessons-learned for constructing knowledge graphs from academia and industry.
Eight papers were submitted. The reviews were open and public, and hosted at Open Review6 .
Each paper received at least three reviews from reviewers with different background and status.
Each paper received a review from a senior, a junior and an industry researcher.
Five papers were accepted and one was conditionally accepted. Five of the accepted papers
were long papers and one was a short paper. The following papers were accepted for publication
and presented at the workshop:
• Not Everybody Speaks RDF: Knowledge Conversion between Different Data Representa-
tions [16].
• BURPing Through RML Test Cases [17].
• Propagating Ontology Changes to Declarative Mappings in Construction of Knowledge
Graphs [18].
• RML-view-to-CSV: A Proof-of-Concept Implementation for RML Logical Views [19].
• R2[RML]-ChatGPT Framework [20].
• Towards Self-Configuring Knowledge Graph Construction Pipelines using LLMs - A Case
Study with RML [21].
During the workshop, the second edition of the Knowledge Graph Construction Challenge
was organized with two different tracks: (i) conformance with the new RML modules, and (ii)
performance of engines on the same hardware.
The first track around conformance with the new RML modules encouraged developers of
RML engines to support the specifications of the new RML modules by evaluating their engines
5
http://w3id.org/kg-construct/workshop/2024
6
https://openreview.net/group?id=eswc-conferences.org/ESWC/2024/Workshop/KGCW
against 365 test cases provided by the maintainers of each RML module. RML-Core (238 test
cases), which focus on the core parts of RDF generation, provides the biggest number of test
cases, followed by RML-IO (67 test cases) to access various data sources and targets. Data
transformations with FnO were also present through the RML-FNML (13 test cases) module.
Newer modules e.g. RML-Star (18 test cases) for RDF-Star support and RML-CC (29 test cases)
to generate RDFS Collections & Containers provided new challenges for existing engines as
they impact the RDF generation process. We had 5 participating engines for the first track:
RMLMapper [2], SDM-RDFizer [5], mapping-template [16], RPT/SANSA [22], and BURP [17].
The second track around performance was similar to the previous edition except that now
each participant had access to a common hardware environment. This way, each engine had
the same restrictions regarding CPU and RAM. Through this track, we wanted to not only
focus on execution time but also resource consumption of each engine. This track consisted
of 2 parts: (i) artificial data for analyzing specific parameters of the construction process e.g.
joins, data size, mappings, and (ii) real-life data of the GTFS Madrid Benchmark to evaluate
approaches in real use cases. We had 6 participating engines for the second track: mapping-
template [16], FlexRML [23], RMLWeaver-js [24], RPT/Sansa [22], RMLStreamer [25], and
RML-view-to-CSV+RMLStreamer [19].
Several participants also submitted a report of their participation in one or both tracks. The
following reports are included in the proceedings:
• RMLStreamer supported by RML-view-to-CSV in the Performance Track of the KGCW
Challenge 2024 [26].
• RMLWeaver-JS: An Algebraic Mapping Engine in the KGCW Challenge 2024 [24].
• Performance Results of FlexRML in the KGCW Challenge 2024 [27].
• Backwards or Forwards? [R2]RML Backwards Compatibility in RMLMapper [28].
• The Conformance of an RML Processor Built from Scratch to Validate RML Specifications
and Test Cases [29].
• Results for Knowledge Graph Creation Challenge 2024: SDM-RDFizer [30].
• KGCW2024 Challenge Report: RDFProcessingToolkit [31].
Organizing Committee
• David Chaves-Fraga, Universidade de Santiago de Compostela
• Anastasia Dimou, KU Leuven, Flanders Make, Leuven.AI
• Dylan Van Assche, Ghent University – imec – IDLab
• Ana Iglesias-Molina, Universidad Politécnica de Madrid
• Umutcan Serles, University of Innsbruck
Program Committee
• Anelia Kurteva, Delft University of Technology
• Beatriz Esteves, Universidad Politécnica de Madrid
• Ben De Meester, Ghent University – imec – IDLab
• Bram Steenwinckel, Ghent University – imec – IDLab
• Christophe Debruyne, Liège University
• Claus Stadler, University of Leipzig
• Davide Lanti, Free University of Bozen
• Edna Ruckhaus Magnus, Universidad Politécnica de Madrid
• Els de Vleeschauwer, Ghent University
• Enrique Antonio Iglesias, Leibniz University of Hannover
• Ernesto Jimenez-Ruiz, City, University of London
• Femke Ongenae, Ghent University
• Franck Michel, CNRS
• Gertjan De Mulder, Ghent University – imec – IDLab
• Giorgos Flouris, FORTH-ICS
• Hannes Voigt, TU Dresden
• Herminio García-González, Kazerne Dossin
• Ibai Guillén-pacho, Universidad Politécnica de Madrid
• Ioannis Dasoulas, KU Leuven
• Jakub Klímek, Charles University
• Juliette Opdenplatz, Universität Innsbruck
• Jürgen Umbrich, Vienna University of Economics and Business
• Manolis Koubarakis, National and Kapodistrian University of Athens
• Maria-Esther Vidal, Leibniz University of Hannover
• Mario Scrocca, Cefriel
• Markus Schröder, German Research Center for AI
• Michael Freund, Fraunhofer
• Oscar Corcho, Universidad Politécnica de Madrid
• Pano Maria, Skemu
• Samaneh Jozashoori, metaphacts GmbH
• Sergio José Rodríguez Méndez, Australian National University
• Sitt Min Oo, Ghent University – imec – IDLab
• Sven Lieber, Royal Library Of Belgium
• Tobias Schweizer, SWITCH
• Vladimir Alexiev, Ontotext
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