Semantic annotation of mutable data

Electronic annotation of scientific data is very similar to annotation of documents. Both types of annotation amplify the original object, add related knowledge to it, and dispute or support assertions in it. In each case, annotation is a framework for discourse about the original object, and, in ea...

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Bibliographic Details
Published in:PloS one 2013-11, Vol.8 (11), p.e76093-e76093
Main Authors: Morris, Robert A, Dou, Lei, Hanken, James, Kelly, Maureen, Lowery, David B, Ludäscher, Bertram, Macklin, James A, Morris, Paul J
Format: Article
Language:English
Subjects:
Annotations
Biodiversity
Biological diversity
Computational Biology
Computer science
Controlled vocabularies
Data models
Data processing
Data quality control
Genomes
Humans
Information Dissemination
Information management
Information Storage and Retrieval
International conferences
Internet resources
Metadata
Missing data
Molecular Sequence Annotation
Museums
Ontology
Publishing
Quality Control
Semantics
Software
Terminology
Vocabulary, Controlled
Web Ontology Language-OWL
Zoology
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Summary:Electronic annotation of scientific data is very similar to annotation of documents. Both types of annotation amplify the original object, add related knowledge to it, and dispute or support assertions in it. In each case, annotation is a framework for discourse about the original object, and, in each case, an annotation needs to clearly identify its scope and its own terminology. However, electronic annotation of data differs from annotation of documents: the content of the annotations, including expectations and supporting evidence, is more often shared among members of networks. Any consequent actions taken by the holders of the annotated data could be shared as well. But even those current annotation systems that admit data as their subject often make it difficult or impossible to annotate at fine-enough granularity to use the results in this way for data quality control. We address these kinds of issues by offering simple extensions to an existing annotation ontology and describe how the results support an interest-based distribution of annotations. We are using the result to design and deploy a platform that supports annotation services overlaid on networks of distributed data, with particular application to data quality control. Our initial instance supports a set of natural science collection metadata services. An important application is the support for data quality control and provision of missing data. A previous proof of concept demonstrated such use based on data annotations modeled with XML-Schema.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0076093