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SeWeBMeDA 2020 : 4th workshop on Semantic Web solutions for large-scale biomedical data analytics

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Link: https://sites.google.com/view/sewebmeda-2020/home
 
When Nov 7, 2020 - Nov 7, 2020
Where Virtual
Abstract Registration Due Jul 10, 2020
Submission Deadline Jul 20, 2020
Notification Due Aug 20, 2020
Final Version Due Sep 10, 2020
Categories    semantic web   linked data   biomedical   data analytics
 

Call For Papers

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 in flowing 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 offer data analytics benefits for decision support. Especially in combination with Semantic Web and Linked Data technologies, these promises to enable the processing of large as well as semantically heterogeneous data sources and the capturing of 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. 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.

Topics of interest include, but are not limited to Semantic Web and Linked Data technologies in the following areas:


Techniques for analysing semantic data in the life sciences, medicine and health care

The description, integration, analysis and use of data in pursuit of challenges in the life sciences, medicine and health

Tools and applications for biomedical and life sciences

Large scale biomedical data curation and integration

Processing biomedical data at scale

Knowledge representation and knowledge discovery for biomedical data

Data and metadata publishing, profiling and new datasets in biomedical and life sciences

Question answering and dialogues over biomedical and life science Linked Data, Ontologies and Knowledge Graphs–Querying and federating data over heterogeneous datasources

FAIR (Findable, Accessible, Interoperable and Reusable) publishing, usage and analysis of biomedical/ life science data

Scalable integration and reproducible analysis of FAIR (Findable, Accessible, Interoperable and Reusable) data

Virtual and Augmented Reality in Biomedical/ Life Science education and applications

Cleaning, quality assurance, and provenance of Semantic Web data, services, and processes in Biomedical/ Life Science

Querying and federating data over heterogeneous datasources

Biomedical ontology creation, mapping/ matching/ translation and reconciliation

Biomedical Ontology and data visualization

Building and maintaining biomedical knowledge graphs

Machine learning with biomedical knowledge graphs

Knowledge Graphs and Relational Learning for Life Sciences

Intelligent Visualizations of Linked Life Science Data

Biomedical data quality assessment and improvement

From Semantics to Explanations in biomedicine and life science

Text analysis, text mining and reasoning using semantic technologies

New technologies and exploitation of existing ones in Linked Data and Semantic Web

Social, ethical and moral issues publishing and consuming biomedical and life sciences data.



Proceedings
The Proceedings of SeWeBMeDA-2020 are planned to published at CEUR Workshop Proceedings



Journal of Biomedical Semantics


Top selected manuscripts will be invited for submitting paper for the special call at "Journal of Biomedical Semantics"

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