This workshop solicits contributions that advance the theory, systems, and applications of semantic technologies for data management. Topics of interest include, but are not limited to:
Knowledge Graphs (KGs) and Virtual Knowledge Graphs (VKGs) / Ontology-Based Data Access (OBDA).
Ontology-mediated query answering, query rewriting, and complexity/tractability results.
(V)KG construction, refinement, enrichment,and schema/mapping design.
Provenance, explainability, and accountability for (V)KG query answers.
Ontologies, metadata vocabularies, and standards.
Temporal, spatial, and other non-standard KG modalities.
Performance, scalability, distributed execution, and security for (V)KGs.
Evolution, updates, and preservation of (V)KGs (consistency and incremental reasoning).
Intersections with machine learning, neural symbolic methods, and LLM-augmented pipelines.
(V)KG analytics, benchmarking, and evaluation methodologies.
Link to the submission system: TBD.
The ST4DM reviewing is single-blind, so the names of the authors will be visible to the reviewers and should be indicated on the submitted files.
Submissions within the workshop scope may be of three types:
Full research paper: Submitted papers must not exceed 14 pages including the bibliography, and must include an abstract of no more than 300 words. Please, note that the minimum length is 10 pages.
Short paper: Submitted papers must not exceed 9 pages including the bibliography, and must include an abstract of no more than 300 words. Please, note that the minimum length is 5 pages (including the bibliography).
Abstract: should be 2-4 pages long including the bibliography.
Submissions will be judged solely based on their content.
Both regular papers, short papers and abstracts must be formatted using the new CEUR template (containing the self-declaration on the use of generative AI). Please use 1-column style.
Accepted submissions will be presented during the workshop.
Outstanding papers may be invited to submit an extended version of their contribution to a journal special issue.
The ST4DM reviewing is single-blind, so the names of the authors will be visible to the reviewers and should be indicated on the submitted files.