About the Workshop

The life sciences domain has been an early adopter of linked data and, a considerable portion of the Linked Open Data cloud is composed of life sciences data sets. The deluge of inflowing biomedical data, partially driven by high-throughput gene sequencing technologies, is a key contributor and motor to these developments. The available data sets require integration according to international standards, large-scale distributed infrastructures, specific techniques for data access, and data analytics. In combination, Semantic Web and Linked Data technologies, promise to enable the processing of large and semantically heterogeneous data sources capturing new knowledge from those.

This workshop invites papers for life sciences and biomedical data processing, as well as the amalgamation with Linked Data and Semantic Web technologies for better data analytics, knowledge discovery and user-targeted applications. SeWeBMeDA provides a forum to explore novel approaches involving semantic web technologies, linked data, and artificial data for integrating, representing, and analyzing biomedical data, with focus on scalable infrastructure, knowledge representation, and advanced analytics that support both research and clinical applications.

This workshop seeks original contributions describing theoretical and practical methods and techniques that present the anatomy of large-scale linked data infrastructure, which covers: the distributed infrastructure to consume, store and query large volumes of heterogeneous linked data; using indexes and graph aggregation to better understand large linked data graphs, query federation to mix internal and external data sources, and linked data visualisation tools for health care and life sciences. It will further cover topics around data integration, data profiling, data curation, querying, knowledge discovery, ontology mapping/matching/reconciliation and data/ontology visualisation, applications/tools /technologies/techniques for life sciences and biomedical domain. SeWeBMeDA aims to provide researchers in biomedical and life science, an insight and awareness about large-scale data technologies for linked data, which are becoming increasingly important for knowledge discovery in the life sciences domain.

Happy to announce: 9th Intl Workshop on Semantic Web solutions for large-scale biomedical data analytics@sewebmeda is coming with @eswc_conf in Dubrovnik, Croatia May 2026.
More details to be announced soon.@SMAliHasnain @micheldumontier @RitaTorresSousa @RCSI_Irl

— SeWeBMeDA (@sewebmeda) December 19, 2025

Topics of interest include, but are not limited to the following areas:

1. Data Integration
   • Dataspaces, data warehouses, and database solutions in healthcare and life sciences
   • Large-scale curation, integration, processing, and analysis of heterogeneous biomedical data
   • Cleaning, quality assurance, and provenance tracking for life sciences data
   • Biomedical data quality assessment and improvement
   • Implementation, governance, and assessment of FAIR data principles in life sciences
   • Data and metadata publishing, profiling, and discovery of new biomedical datasets
   • Data streams, Internet of Things, mobile platforms, cloud environment in life sciences
2. Knowledge Representation
   • Biomedical ontology creation, mapping/ matching/ translation and reconciliation
   • Building and maintaining knowledge graphs in healthcare and life sciences
   • Knowledge graph enrichment using text mining and natural language processing techniques
   • Visualization and exploration of linked data, ontologies, and knowledge graphs in life sciences
3. AI/ML/Reasoning over Ontologies and Knowledge Graphs
   • ML and relational learning over biomedical knowledge graphs
   • Neurosymbolic AI and hybrid reasoning approaches for biomedical data
   • Large language models integrated with biomedical knowledge graphs
   • Explainable AI approaches leveraging semantic technologies in life sciences
4. Applications and Use Cases
   • Semantic technologies supporting research and clinical applications in life sciences
   • Querying and federating data over heterogeneous data sources
   • Virtual and augmented reality in life sciences education and applications
   • Generative AI and conversational AI applications in life sciences
5. Ethical, Social, and Practical Considerations
   • Risks and opportunities of using Semantic Web and AI technologies in life sciences
   • Bias and fairness in AI systems for healthcare and life sciences
   • Challenges of explainability and accountability in AI systems for healthcare and life sciences
   • Responsible development of neuro-symbolic and generative AI in clinical workflows

Proceedings

The Proceedings of SeWeBMeDA-2026 are planned to be published at CEUR Workshop Proceedings.