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DQMLKG 2024 : Data Quality meets Machine Learning and Knowledge Graphs: Bridging Precision with Intelligence

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Link: https://dqmlkg.github.io
 
When May 26, 2024 - May 27, 2024
Where Hersonissos, Greece
Submission Deadline Feb 26, 2024
Notification Due Mar 28, 2024
Final Version Due Apr 18, 2024
Categories    semantic web   data quality   machine learning
 

Call For Papers

This workshop aims to explore the intricate interplay of data quality, ML, and KGs, elucidating limitations in assessment methodologies, proposing effective methods for objective quality assessment, and addressing challenges on ML and AI in general, verify if and to what extent well-known quality metrics are compliant with ML-based quality assessment, and addressing FAIR principles. We also welcome proposals riding the path of Explainable AI, Large Language Models, Generative AI, and any AI-driven approach that can be applied to the Semantic Web technologies to support and enhance data quality assessment and improvement.

=Submission details=

* Full research papers (up to 15 pages, excluding references)
* Short research papers (up to 8 pages, excluding references)

Papers must comply with the CEUR-WS template. Papers are submitted in PDF format via the workshop’s Open Review submission page https://openreview.net/group?id=eswc-conferences.org/ESWC/2024/Workshop/DQMLKG.

=Important dates=

* Paper submission deadline: February 26, 2024 (11:59 pm, Hawaii time)
* Notification of Acceptance: March 28, 2024 (11:59 pm, Hawaii time)
* Camera-ready paper due: April 18, 2024 (11:59 pm, Hawaii time)

=Topics of interest (but are not limited to)=

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 KGs quality issues

We are looking forward to your contribution!

In case you have additional questions concerning the submission process, please do not hesitate to contact @MariaAngelaPellegrino - mapellegrino@unisa.it

We are looking forward to your contribution!

Anisa Rula,
Maria Angela Pellegrino,
Michael Cochez,
Jose Emilio Labra Gayo and
Mehwish Alam
Workshop organisers

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