=Paper=
{{Paper
|id=Vol-2873/preface
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2873/preface.pdf
|volume=Vol-2873
}}
==None==
https://ceur-ws.org/Vol-2873/preface.pdf
Preface for the 2nd Edition of the International
Knowledge Graph Construction Workshop
David Chaves-Fraga1 , Anastasia Dimou2 , Pieter Heyvaert2 ,
Freddy Priyatna3 , and Juan Sequeda4
1
Ontology Engineering Group, Universidad Politécnica de Madrid
dchaves@fi.upm.es
2
IDLab, Dept of Electronics and Information Systems, Ghent University – imec
{anastasia.dimou,pheyvaer.heyvaert}@ugent.be
3
Olive AI
freddy.priyatna@oliveai.com
4
data.world
juan@data.world
More and more knowledge graphs are constructed for private use, e.g., the
Amazon Product Graph [1] or the Fashion Knowledge Graph by Zalando5 ,or
public use, e.g., DBpedia6 or Wikidata7 . While techniques to automatically con-
struct KGs from existing Web objects exist (e.g., scraping Web tables), there is
still room for improvement. So far, constructing knowledge graphs was consid-
ered an engineering task, however, more scientifically robust methods keep on
emerging. These methods were widely questioned for their verbosity, low perfor-
mance 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. Nev-
ertheless constructing knowledge graphs is still not a straightforward task be-
cause several existing challenges remain and yet the barriers to construct knowl-
edge graphs are not lowered enough to be easily and broadly adopted by indus-
try. These reasons and the vastly populated knowledge graph construction W3C
Community Group8 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], FunMap [7] and CARML9 , while others the
query-answering paradigm, e.g., Ultrawrap [8], Morph-RDB [9] and Ontop [10].
5
https://engineering.zalando.com/posts/2018/03/
semantic-web-technologies.html
6
https://www.dbpedia.org/resources/knowledge-graphs/
7
https://www.wikidata.org/wiki/Wikidata:Main_Page
8
http://w3.org/community/kg-construct
9
https://github.com/carml/carml
Besides R2RML-based extensions, alternatives were proposed, e.g., SPARQL-
Generate [11] and ShExML [12], as well as methods to perform data transfor-
mations while constructing knowledge graphs, e.g., FnO [13] and FunUL [14].
The second edition of the knowledge graph construction workshop10 has a
special focus on knowledge graph construction methods that involve or analyze
the roles of users in these processes and it also included:
– Mapping Challenges. As the workshop complements and aligns with the ac-
tivities of the W3C Community Group on knowledge graph construction,
a special track for solving a set of well-identified mapping challenges11 was
announced and different solutions were proposed.
– Keynote. The workshop includes the keynote from Jesús Barrasa (Neo4J):
“Knowledge graphs 2021: The great convergence”
– Discussion Panel. A panel on machine learning techniques for knowledge
graph construction was organized with distinguish researchers invited as
panelist: Ernesto Jiménez-Ruiz, Franceso Osborne, Maria-Esther Vidal and
Heiko Paulheim.
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.
Sixteen papers were submitted, one of which was withdrawn. The reviews
were open and public, and hosted at Open Review12 . 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.
Twelve papers were accepted and one was conditionally accepted. Eight of
the accepted papers were long papers and four were short papers. The following
papers were accepted for publication and presented at the workshop:
– Everything for the Users, Nothing by the Users: Lessons Learnt from an
Heterogeneous Data Mapping Languages User Study [15]
– A ShExML Perspective on Mapping Challenges: Already Solved Ones, Lan-
guage Modifications and Future Required Actions [16]
– Mapping Spreadsheets to RDF: Supporting Excel in RML [17]
– Demo: Knowledge Graph-Based Housing Market Analysis [18]
– JenTab: A Toolkit for Semantic Table Annotations [19]
– Stratified Data Integration [20]
– Collaborative-AI Knowledge Graph Generation: Taxonomization of IATE,
the EU Terminology [21]
– Embedding-Assisted Entity Resolution for Knowledge Graphs [22]
– Integrating Nested Data Into Knowledge Graphs with RML Fields [23]
– Open Drug Knowledge Graph [24]
10
http://w3id.org/kg-construct/workshop/2021
11
http://w3id.org/kg-construct/workshop/2021/challenges
12
https://openreview.net/group?id=eswc-conferences.org/ESWC/2021/
Workshop/KGCW
2
– Knowledge Graph Construction with R2RML and RML: An ETL System-
Based Overview [25]
– Knowledge Graph Lifecycle: Building and maintaining Knowledge Graphs [26]
– Experiences of Using WDumper to Create Topical Subsets from Wikidata [27]
Organizing Committee
– David Chaves-Fraga, Universidad Politécnica de Madrid
– Anastasia Dimou, Ghent University - imec
– Pieter Heyvaert, Ghent University - imec
– Freddy Priyatna, Olive AI
– Juan Sequeda, data.world
Program Committee
– Aidan Hogan, Universidad de Chile
– Ana Iglesias-Molina, Universidad Politécnica de Madrid
– Antoine Zimmermann, École des Mines de Saint-Étienne
– Ben De Meester, Ghent University – imec
– Boris Villazón-Terrazas, Tinámica
– Dylan Van Assche, Ghent University – imec
– Edna Ruckhaus, Universidad Politécnica de Madrid
– Femke Ongenae, Ghent University – imec
– Francesco Osborne, The Open University
– Franck Michel, Université Côte d’Azur
– François Scharffe, Columbia University
– George Fletcher, TU Eindhoven
– Giorgos Flouris, FORTH
– Giuseppe Futia, Nexa Center
– Guohui Xiao, Ontopic
– Hannes Voigt, Neo4j
– Heiko Paulheim, University of Mannheim
– Herminio Garcia Gonzalez, Universidad de Oviedo
– Jakub Klímek, Charles University
– Josh Shinavier, Uber
– Julián Arenas-Guerrero, Universidad Politécnica de Madrid
– Manolis Koubarakis, National & Kapodistrian University of Athens
– Maria-Esther Vidal, L3S & TIB
– Mario Scrocca, CEFRIEL
– Mauro Dragoni, FBKZ
– Maxime Lefrancois, École des Mines de Saint-Étienne
– Miel Vander Sande, Memoo
– Mohamed Nadjib Mami, Deutsche Post DHL Group
– Oscar Corcho, Universidad Politécnica de Madrid
– Pano Maria, Skemu
3
– Samaneh Jozashoori, L3S & TIB
– Semih Salihoglu, University of Waterloo
– Souripriya Das, Oracle
– Sven Lieber, Ghent University – imec
– Umutcan Şimşek, University of Innsbruck
– Vladimir Alexiev, Ontotext
References
[1] Xin Luna Dong et al. “AutoKnow: Self-Driving Knowledge Collection for
Products of Thousands of Types”. In: KDD ’20. Virtual Event, CA, USA:
Association for Computing Machinery, 2020, pp. 2724–2734. isbn: 9781450379984.
doi: 10 . 1145 / 3394486 . 3403323. url: https : / / doi . org / 10 . 1145 /
3394486.3403323.
[2] Anastasia Dimou et al. “RML: A Generic Language for Integrated RDF
Mappings of Heterogeneous Data”. In: Proceedings of the 7th Workshop on
Linked Data on the Web (LDOW). 2014.
[3] David Chaves-Fraga et al. “Virtual Statistics Knowledge Graph Generation
from CSV files”. In: Emerging Topics in Semantic Technologies: ISWC
2018 Satellite Events. Studies on the Semantic Web. IOS Press, 2018.
[4] Franck Michel et al. xR2RML: Relational and Non-Relational Databases
toRDF Mapping Language. Tech. rep. 2017. url: https://hal.archives-
ouvertes.fr/hal-01066663/document/.
[5] Enrique Iglesias et al. “SDM-RDFizer: An RML Interpreter for the Effi-
cient Creation of RDF Knowledge Graphs”. In: Proceedings of the 29th
ACM International Conference on Information & Knowledge Manage-
ment. 2020, pp. 3039–3046.
[6] Umutcan Şimşek, Elias Kärle, and Dieter Fensel. “RocketRML - A NodeJS
implementation of a Use-Case Specific RML Mapper”. In: Proceedings of
the 1st Workshop on Knowledge Graph Building. 2019.
[7] Samaneh Jozashoori et al. “FunMap: Efficient Execution of Functional
Mappings for Knowledge Graph Creation”. In: International Semantic Web
Conference. Springer. 2020, pp. 276–293.
[8] Juan F. Sequeda and Daniel P. Miranker. “Ultrawrap: SPARQL execution
on relational data”. In: Web Semantics: Science, Services and Agents on
the WWW (2013). issn: 1570-8268. doi: https://doi.org/10.1016/j.
websem.2013.08.002. url: http://www.sciencedirect.com/science/
article/pii/S1570826813000383.
[9] Freddy Priyatna, Oscar Corcho, and Juan Sequeda. “Formalisation and
Experiences of R2RML-based SPARQL to SQL Query Translation Using
Morph”. In: Proceedings of the 23rd International Conference on World
Wide Web. 2014.
[10] Diego Calvanese et al. “Ontop: Answering SPARQL Queries over Rela-
tional Databases”. In: Semantic Web Journal (2017).
4
[11] Maxime Lefrançois, Antoine Zimmermann, and Noorani Bakerally. “A
SPARQL Extension for Generating RDF from Heterogeneous Formats”.
In: The Semantic Web: 14th International Conference. 2017.
[12] Herminio García-González et al. “ShExML: improving the usability of het-
erogeneous data mapping languages for first-time users”. In: PeerJ Com-
puter Science 6 (2020), e318.
[13] Ben De Meester et al. “An ontology to semantically declare and describe
functions”. In: European Semantic Web Conference. Springer. 2016, pp. 46–
49.
[14] Ademar Crotti Junior et al. “FunUL: a method to incorporate functions
into uplift mapping languages”. In: Proceedings of the 18th International
Conference on Information Integration and Web-based Applications and
Services. 2016, pp. 267–275.
[15] Herminio García-González. “Everything for the users, nothing by the users:
Lessons learnt from an heterogeneous data mapping languages user study”.
In: Proceedings of the 2nd International Workshop on Knowledge Graph
Construction. 2021.
[16] Herminio García-González. “A ShExML perspective on mapping challenges:
already solved ones, language modifications and future required actions”.
In: Proceedings of the 2nd International Workshop on Knowledge Graph
Construction. 2021.
[17] Markus Schröder, Christian Jilek, and Andreas Dengel. “Mapping Spread-
sheets to RDF: Supporting Excel in RML”. In: Proceedings of the 2nd
International Workshop on Knowledge Graph Construction. 2021.
[18] Ziping Hu et al. “Demo: Knowledge Graph-Based Housing Market Anal-
ysis”. In: Proceedings of the 2nd International Workshop on Knowledge
Graph Construction. 2021.
[19] Nora Abdelmageed and Sirko Schindler. “JenTab: A Toolkit for Semantic
Table Annotations”. In: Proceedings of the 2nd International Workshop on
Knowledge Graph Construction. 2021.
[20] Fausto Giunchiglia et al. “Stratified Data Integration”. In: Proceedings of
the 2nd International Workshop on Knowledge Graph Construction. 2021.
[21] Alena Vasilevich et al. “Collaborative-AI Knowledge Graph Generation:
Taxonomization of IATE, the EU Terminology”. In: Proceedings of the 2nd
International Workshop on Knowledge Graph Construction. 2021.
[22] Daniel Obraczka, Jonathan Schuchart, and Erhard Rahm. “Embedding-
Assisted Entity Resolution for Knowledge Graphs”. In: Proceedings of the
2nd International Workshop on Knowledge Graph Construction. 2021.
[23] Thomas Delva et al. “Integrating Nested Data into Knowledge Graphs
with RML Fields”. In: Proceedings of the 2nd International Workshop on
Knowledge Graph Construction. 2021.
[24] Mark Mann et al. “Open Drug Knowledge Graph”. In: Proceedings of the
2nd International Workshop on Knowledge Graph Construction. 2021.
[25] Julián Arenas-Guerrero et al. “Knowledge Graph Construction with R2RML
and RML: An ETL System-Based Overview”. In: Proceedings of the 2nd
International Workshop on Knowledge Graph Construction. 2021.
5
[26] Umutcan Şimşek et al. “Knowledge Graph Lifecycle: Building and main-
taining Knowledge Graphs”. In: Proceedings of the 2nd International Work-
shop on Knowledge Graph Construction. 2021.
[27] Seyed Amir Hosseini Beghaeiraveri, Alasdair Gray, and Fiona McNeill.
“Experiences of Using WDumper to Create Topical Subsets from Wiki-
data”. In: Proceedings of the 2nd International Workshop on Knowledge
Graph Construction. 2021.
6