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BioSPARQL: ontology-based smart building of SPARQL queries for biological linked open data

Published:07 December 2011Publication History

ABSTRACT

Building an efficient SPAQRL query over the great variety of copious bio-medical Linked Open Data (LOD) requires users to understand the data schema, and makes it difficult for biologists to handle such data. To address this problem, we challenge to realise a SPARQL query builder that generates a structurally-optimised query by logically analysing the target RDF/OWL data; but still the corresponding unifying logic over RDF/OWL data needs to be implemented. An ontology-based smart SPARQL query builder named Bio-SPARQL is an implementation of the needed logic. Bio-SPARQL generates structurally-optimised queries over an ontologically classified RDF/OWL based bio-medical LOD by logically analysing their semantic graph structure. Bio-SPARQL employs our database named BioLOD having LOD data sets categorised in 744 classes with 7.88 million data items (instances) integrated public various types of omic databases by human curation and provides a set of LOD data files of each class. To aid in writing a query, it provides a graphical user interface that suggests possible data path schema and filters by analysing its corresponding ontological BioLOD data structure. The generated SPARQL query is designed to be performed in a user's local environment with its corresponding downloaded BioLOD data files in order to control the influence on query results due to data updates.

References

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  1. BioSPARQL: ontology-based smart building of SPARQL queries for biological linked open data

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              cover image ACM Other conferences
              SWAT4LS '11: Proceedings of the 4th International Workshop on Semantic Web Applications and Tools for the Life Sciences
              December 2011
              139 pages
              ISBN:9781450310765
              DOI:10.1145/2166896

              Copyright © 2011 Copyright is held by the owner/author(s)

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 7 December 2011

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