Application of Ontologies and Semantic Web Technologies in Biomedical Science
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 10074
Special Issue Editors
Interests: semantic interoperability; electronic health record standards; knowledge representation; semantic web; biomedical ontologies; SNOMED CT
Interests: semantic web; ontologies; biomedical semantics; semantic interoperability; ontology engineering
Special Issue Information
Dear Colleagues,
Biomedical researchers face the challenge to integrate and analyze growing amounts of data across syntactically and semantically heterogeneous data sources. Semantic Web technologies and ontologies in particular are the main vehicle for data sharing, integration, and reuse. They enable the formal representation of data meaning, which is indispensable for their unambiguous interpretation.
Examples of prominent ontologies in the biomedical science are gene ontology and SNOMED CT. In addition, the OBO Foundry community and the BioPortal repository are two initiatives that show the increasing adoption of ontologies and Semantic Web technologies in the biomedical domain.
Linked data and knowledge graphs are also key components of the Semantic Web. While the former pursues the open sharing of data through RDF graphs, knowledge graphs have a less open perspective and have already found a place in industry with companies such as Google, Microsoft, IBM, etc.
Ontologies and Semantic Web technologies are related to many disciplines such as knowledge representation and reasoning or databases and are being applied successfully to other fields such as natural language processing or machine learning.
This Special Issue aims to reflect the state of the art in these technologies and to provide guidance on future research directions. Submissions include research papers, case studies, and reviews.
Topics of interest include but are not limited to the following areas:
- Knowledge representation and reasoning;
- Ontology development and enrichment;
- Ontology and linked data set quality assurance;
- Semantic harmonization and ontology alignment;
- Knowledge graphs;
- Knowledge representation systems in life sciences and medicine;
- NLP and text mining using semantic technologies;
- Novel approaches for data integration of heterogeneous data sources;
- Novel tools and ontologies for data interpretation and visualization;
- Deep learning and semantic technologies;
- Real-world ontology-based applications;
- Methods and tools for the FAIRification of datasets;
- Biomedical research objects;
- Industry use cases;
- Artificial intelligence techniques for the biomedical Semantic Web;
- Ontologies and Semantic web for decision support;
- Semantic technologies and AI explainability in biomedicine.
Prof. Dr. Catalina Martinez-Costa
Prof. Dr. Jesualdo Tomás Fernández Breis
Guest Editors
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