Organizing phenotypic data—a semantic data model for anatomy

• Published in: Journal of biomedical semantics Vol. 10; no. 1; pp. 12 - 14
• Main Author: Vogt, Lars
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.06.2019; BioMed Central; BMC
• Subjects: Analysis; Anatomy; Anatomy, ontology; Big data; Biodiversity; Biological evolution; Biological Ontologies; Computer Graphics; Computer simulation; Computers; Descriptions; Genomics; Genotype & phenotype; Graph theory; Graphs; Informatics; Instance anatomy knowledge graph; Knowledge management; Life sciences; Metadata; Morphology; Ontology; Phenotype; Phenotypic data; Phylogenetics; Recording; Resource Description Framework-RDF; Semantic data model for anatomy; Semantic web; Semantics; Syntax; Technology; Terminology; Terms and phrases; Unstructured data; Web Ontology Language-OWL; Zoology; Morphological description; Anatomy, ontology; Morphology; Zoology; Semantic data model for anatomy; Knowledge management; Instance anatomy knowledge graph; Phenotypic data
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-019-0204-6

Currently, almost all morphological data are published as unstructured free text descriptions. This not only brings about terminological problems regarding semantic transparency, which hampers their re-use by non-experts, but the data cannot be parsed by computers either, which in turn hampers their integration across many fields in the life sciences, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. With an ever-increasing amount of available ontologies and the development of adequate semantic technology, however, a solution to this problem becomes available. Instead of free text descriptions, morphological data can be recorded, stored, and communicated through the Web in the form of highly formalized and structured directed graphs (semantic graphs) that use ontology terms and URIs as terminology. After introducing an instance-based approach of recording morphological descriptions as semantic graphs (i.e., Semantic Instance Anatomy Knowledge Graphs) and discussing accompanying metadata graphs, I propose a general scheme of how to efficiently organize the resulting graphs in a tuple store framework based on instances of defined named graph ontology classes. The use of such named graph resources allows meaningful fragmentation of the data, which in turn enables subsequent specification of all kinds of data views for managing and accessing morphological data. Morphological data that comply with the here proposed semantic data model will not only be computer-parsable but also re-usable by non-experts and could be better integrated with other sources of data in the life sciences. This would allow morphology as a discipline to further participate in eScience and Big Data.