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. This research contribution should provide useful information for the Knowledge Acquisition research community as well as the working Data Scientist.

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.

Submission Guideline

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

• Full papers (up to 15 pages): Presenting novel scientific research pertaining to topics relevant to the workshop topics.

• Short papers (up to 8-10 pages): New system and Dataset descriptions, relevant to the topics of interest.

• Demo/Poster papers (up to 4 pages): Describe a demo or poster of a tool on the workshop topics.

• Position Paper (up to 6 - 8 pages).

• European/ International Project Showcase (up to 6-8 pages): Introduce and disseminate the findings from ongoing and/or recently (within the last 24 months of this workshop) finished project.

• Work-in-progress (up to 5-10 pages): Ideal opportunity to submit incomplete results/ trends and receive feedback from experts in the area

List of Topics

Topics of interest include, but are not limited to Web and Data technologies in 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

• Assit Professor Dr. Ali Hasnain 
• Prof Dr. Michel Dumontier
• Dr Dr. Rita T. Sousa

Proceedings

The Proceedings of SeWeBMeDA-2024 are planned to be published at CEUR Workshop Proceedings

Journal submissions: Top selected manuscripts will be invited to submit their paper for the special call at "Journal of Biomedical Semantics"