The 1st edition of the Data Quality meets Machine Learning and Knowledge Graphs
                         (DQMLKG) workshop was held in Hersonissos, Crete, on May 26th, 2024, co-located with
                         the 21st Extended Semantic Web Conference (ESWC 2024).

                         The DQMLKG workshop aimed to explore the intricate interplay of data quality, Machine
                         Learning (ML), and Knowledge Graphs (KGs). This includes elucidating limitations in
                         assessment methodologies, proposing effective methods for objective quality assessment,
                         and addressing challenges on ML and Artificial Intelligence (AI) in general, as well as as
                         verifying if and to what extent well-known quality metrics are compliant with ML-based
                         quality assessment, and addressing FAIR principles. We also welcomed proposals riding the
                         path of Explainable AI, Large Language Models, Generative AI, and any AI-driven approach
                         that can be applied to Semantic Web technologies to support and enhance data quality
                         assessment and improvement.

                         The topics of interest included the following:
                            ● New approaches for performing Data quality assessment or improvement of
                                Knowledge Graphs via Machine Learning
                                    ● Quality assessment over time
                                    ● Scalability issues
                                    ● Proactive approaches able to improve KG quality during the data authoring
                                         stage
                                    ● Reactive approaches to improve KG quality before the data exploitation stage
                                    ● Large Language Models to deal with KG quality issues
                                    ● Generative Artificial Intelligence (AI) to cope with KG quality issues
                                    ● AI-driven approach to assess and improve data quality issues over KGs
                            ● Applications combining Machine Learning and Knowledge Graphs dealing with Data
                                Quality concerns:
                                    ● Recommender Systems leveraging (incomplete) Knowledge Graphs
                                    ● Link Prediction and completing KGs
                                    ● Ontology Learning and Matching coping with KG consistency and accuracy
                                    ● Question Answering exploiting Knowledge Graphs and Machine Learning
                                         dealing with representational issues
                                    ● Domain Specific KG quality issues

                         DQMLKG 2024 opened with an exciting keynote entitled “When stars align: studies in data
                         quality, knowledge graphs, and machine learning” held by Elena Simperl, Professor of
                         Computer at King’s College London and Director of Research for the Open Data Institute.
                         She emphasized that Data quality is the topic all of us should care about.
                         Then, the workshop led to the presentation of four contributions from researchers across
                         Europe and mainly concerned the use of Large Language Models for supporting quality
                         assessment, the impact of Linguistic KGs in dealing with formality, and how to extend shape
                         expressions to approach different types of KGs. All contributions were presented in person
                         at the workshop in Hersonisso, Crete. This encouraged an interesting and engaging
                         discussion and a profitable questions-and-answers session. Finally, the discussion continued
                         with a panel composed of Elena Simperl, Paul Groth, Heiko Paulheim, and Anastasia Dimou
                         with Mehwish Alam as a moderator. This provided all participants with insights about the
                         future of the topic and strongly suggested investing in this direction.


CEUR
                  ceur-ws.org
Workshop      ISSN 1613-0073
Proceedings
All contributions have been published on http://ceur-ws.org as joint proceedings with the 3rd
International Workshop on Knowledge Graph Generation from Text (TEXT2KG). At least two
program committee members reviewed the submissions of DQMLKG.

We want to thank the program committee members, the authors, and all the participants.

The organizers,

Maria Angela Pellegrino (University of Salerno, ITALY),
Anisa Rula (University of Brescia, ITALY),
Jose Emilio Labra Gayo (University of Oviedo, SPAIN),
Michael Cochez (Vrije Universiteit Amsterdam, THE NETHERLANDS),
and Mehwish Alam (Institut Polytechnique de Paris, FRANCE).

Program committee:
   - Cinzia Cappiello, Polytechnic of Milan, Italy
   - Jeremy Debattista, Trinity College Dublin, Ireland
   - Anastasia Dimou, Katholieke Universiteit, Leuven
   - Paul Groth, University of Amsterdam, Holland
   - Antonio Lieto, University of Salerno
   - Ernesto Jiménez-Ruiz, University of London, England
   - Blerina Spahiu, University of Milan, Italy