Abstract
BackgroundCurrently, 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.ResultsAfter 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.ConclusionsMorphological 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.
Highlights
Almost all morphological data are published as unstructured free text descriptions
The morphological record consists for the most part of unstructured text. This has far-reaching consequences for research based on morphological data, since conventional morphological free text descriptions bear problems relating to terminology and lack of semantic transparency, but they cannot be parsed by computers
Organizing a document as a semantic graph In Resource Description Framework (RDF), propositions are structured as triple statements consisting of Subject, Predicate, and Object, with Subject and Predicate being resources in the form of a Uniform Resource Identifier (URI) and the Object, depending on the type of Predicate used in the statement, being either a resource or some numerical or literal value
Summary
Almost all morphological data are published as unstructured free text descriptions This brings about terminological problems regarding semantic transparency, which hampers their re-use by nonexperts, 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. The morphological record consists for the most part of unstructured text This has far-reaching consequences for research based on morphological data, since conventional morphological free text descriptions bear problems relating to terminology and lack of semantic transparency, but they cannot be parsed by computers. Someone interested in using morphological data, for instance, for systematically searching for correlations between phenotypic data and genotypes over a broad set of non-model organisms, will soon be discouraged after briefly having delved into. This data would be openly accessible and findable through the Web, they would be stored in online databases in a highly formalized and structured syntax and format, they would be semantically transparent and easier to comprehend and interpret, and data from different authors and different taxa could be integrated and algorithms could read and analyze them
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