More and more knowledge graphs are constructed for private use, e.g., the Amazon Product 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 dificulty of use, while the data sources' variety and complexity cause further syntax and 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.
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others the query-answering paradigm, e.g., Ultrawrap [
The third edition of the knowledge graph construction workshop5 has a special focus on the automatization of knowledge graph construction methods, analyzing their alignment with previous standard but declarative approaches using mapping rules. It also included: • Keynote. The workshop includes the keynote from Javier D. Fernandez and Selena Baset (Roche): “From ETL to DIY, or how to democratize the creation of Knowledge Graphs” • SemTab challenge 2022.6: Kick-of for the Semantic Web Challenge on Tabular Data to
Knowledge Graph Matching for 2022.
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.
Eleven papers were submitted. The reviews were open and public, and hosted at Open Review7. Each paper received at least three reviews from reviewers with diferent background and status. Each paper received a review from a senior, a junior and an industry researcher.
Six papers were accepted and one was conditionally accepted. two of the accepted papers were long papers and five were short papers. The following papers were accepted for publication and presented at the workshop: • Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in
Social Networks [15] • A Human-in-the-Loop Approach for Personal Knowledge Graph Construction from File
Names [16] • Continuous generation of versioned collection’s members with RML and LDES [17] • Implementation-independent Knowledge Graph Construction Workflows using FnO
Composition [18] • Declarative Description of Knowledge Graphs Construction Automation: Status & Challenges [19] • Devising Mapping Interoperability with Mapping Translation [20] • Supporting Relational Database Joins for Generating Literals in R2RML [21] Organizing Committee 5http://w3id.org/kg-construct/workshop/2022 6https://www.cs.ox.ac.uk/isg/challenges/sem-tab/ 7https://openreview.net/group?id=kg-construct.github.io/KGCW/2022/Workshop Program Committee Acknowledgments This publication is based upon work from COST Action Distributed Knowledge Graphs, supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. http://www.cost.eu/ [10] D. Calvanese, B. Cogrel, S. Komla-Ebri, R. Kontchakov, D. Lanti, M. Rezk, M. RodriguezMuro, G. Xiao, Ontop: Answering SPARQL Queries over Relational Databases, Semantic Web Journal (2017). [11] M. Lefrançois, A. Zimmermann, N. Bakerally, A SPARQL extension for generating RDF from heterogeneous formats, in: The Semantic Web: 14th International Conference, 2017. [12] H. García-González, I. Boneva, S. Staworko, J. E. Labra-Gayo, J. M. C. Lovelle, Shexml: improving the usability of heterogeneous data mapping languages for first-time users, PeerJ Computer Science 6 (2020) e318. [13] B. De Meester, A. Dimou, R. Verborgh, E. Mannens, An ontology to semantically declare and describe functions, in: European Semantic Web Conference, 2016, pp. 46–49. [14] A. C. Junior, C. Debruyne, R. Brennan, D. O’Sullivan, 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] A. Parnami, M. Lee, Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks, in: Proceedings of the 3rd International Workshop on Knowledge Graph Construction, 2022. [16] M. Schröder, C. Jilek, A. Dengel, A Human-in-the-Loop Approach for Personal Knowledge Graph Construction from File Names, in: Proceedings of the 3rd International Workshop on Knowledge Graph Construction, 2022. [17] D. Van Assche, S. M. Oo, J. Rojas, P. Colpaert, Continuous generation of versioned collection’s members with RML and LDES, in: Proceedings of the 3rd International Workshop on Knowledge Graph Construction, 2022. [18] G. De Mulder, B. De Meester, Implementation-independent Knowledge Graph Construction Workflows using FnO Composition, in: Proceedings of the 3rd International Workshop on Knowledge Graph Construction, 2022. [19] A. Dimou, D. Chaves-Fraga, Declarative Description of Knowledge Graphs Construction Automation: Status & Challenges, in: Proceedings of the 3rd International Workshop on Knowledge Graph Construction, 2022. [20] A. Iglesias-Molina, C. Andrea, C. Oscar, Devising Mapping Interoperability with Mapping Translation, in: Proceedings of the 3rd International Workshop on Knowledge Graph Construction, 2022. [21] C. Debruyne, Supporting Relational Database Joins for Generating Literals in R2RML, in: Proceedings of the 3rd International Workshop on Knowledge Graph Construction, 2022.