- From: Anisa Rula <anisa.rula@gmail.com>
- Date: Thu, 19 Feb 2026 18:27:57 +0100
- To: semantic-web@w3.org
- Cc: mapellegrino@unisa.it, jelabra@gmail.com
- Message-ID: <CABiQHAfd9r0ieK6ukVYbQGijyCiX3dk4zGQhi9OV=4yYX-HDpg@mail.gmail.com>
==== ESWC 2026 - 23rd Extended Semantic Web Conference Dubrovnik, Croatia Call for Papers for the QKG - Workshop on Quality of Knowledge Graphs May 10 or 11, 2026https://dataqualityws.github.io/ ==== This workshop focuses on advancing methods, standards, and tools for assessing and improving the quality of Knowledge Graphs and Linked Data. As the Web of Data continues to grow in scale, heterogeneity, and importance for AI systems, ensuring reliable, interoperable, and FAIR data is more critical than ever. The workshop brings together researchers, practitioners, and industry stakeholders to explore quality dimensions, AI-supported evaluation methods, and reusable workflows for building trustworthy, high-quality data ecosystems. QKG combines invited talks, peer-reviewed papers, and an interactive brainstorming session to shape future research directions and community roadmaps. =Submission details= * Full research papers (up to 15 pages, excluding references) * Short research papers (up to 8 pages, excluding references) * Position papers * Negative results papers Papers must comply with the CEUR-WS template. Papers are submitted in PDF format via the workshop submission page https://easychair.org/conferences/?conf=qkg2026. =Important dates= * Paper submission deadline: *March 3, 2026 (11:59 pm, Hawaii time)* * Notification of Acceptance: March 31, 2026 (11:59 pm, Hawaii time) * Camera-ready paper due: April 15, 2026 (11:59 pm, Hawaii time) =Topics of interest (but are not limited to)= *FAIR data and Open Science practices *FAIRness and Bias Detection *Quality-aware data preparation, curation, and integration *Knowledge Graphs Assessment & Refinement *Metadata quality, provenance, and traceability *Multimodal data quality and AI readiness metrics *Domain-specific and general-purpose quality frameworks *Explicability & Diagnosis (even via AI-driven approaches) Bias detection and explainability in data quality *Industrial perspectives on Web of Data quality In case you have additional questions concerning the submission process, please do not hesitate to contact @MariaAngelaPellegrino We are looking forward to your contribution! Anisa Rula, Maria Angela Pellegrino, Jose Emilio Labra Gayo Workshop organisers
Received on Thursday, 19 February 2026 20:08:05 UTC