OBJECTIVES: To develop mechanisms to formulate queries over the semantic representation of cancer-related data services available through the cancer Biomedical Informatics Grid (caBIG). DESIGN: The semCDI query formulation uses a view of caBIG semantic concepts, metadata, and data as an ontology, and defines a methodology to specify queries using the SPARQL query language, extended with Horn rules. semCDI enables the joining of data that represent different concepts through associations modeled as object properties, and the merging of data representing the same concept in different sources through Common Data Elements (CDE) modeled as datatype properties, using Horn rules to specify additional semantics indicating conditions for merging data. Validation In order to validate this formulation, a prototype has been constructed, and two queries have been executed against currently available caBIG data services. DISCUSSION: The semCDI query formulation uses the rich semantic metadata available in caBIG to build queries and integrate data from multiple sources. Its promise will be further enhanced as more data services are registered in caBIG, and as more linkages can be achieved between the knowledge contained within caBIG's NCI Thesaurus and the data contained in the Data Services. CONCLUSION: semCDI provides a formulation for the creation of queries on the semantic representation of caBIG. This constitutes the foundation to build a semantic data integration system for more efficient and effective querying and exploratory searching of cancer-related data.
OBJECTIVES: To develop mechanisms to formulate queries over the semantic representation of cancer-related data services available through the cancer Biomedical Informatics Grid (caBIG). DESIGN: The semCDI query formulation uses a view of caBIG semantic concepts, metadata, and data as an ontology, and defines a methodology to specify queries using the SPARQL query language, extended with Horn rules. semCDI enables the joining of data that represent different concepts through associations modeled as object properties, and the merging of data representing the same concept in different sources through Common Data Elements (CDE) modeled as datatype properties, using Horn rules to specify additional semantics indicating conditions for merging data. Validation In order to validate this formulation, a prototype has been constructed, and two queries have been executed against currently available caBIG data services. DISCUSSION: The semCDI query formulation uses the rich semantic metadata available in caBIG to build queries and integrate data from multiple sources. Its promise will be further enhanced as more data services are registered in caBIG, and as more linkages can be achieved between the knowledge contained within caBIG's NCI Thesaurus and the data contained in the Data Services. CONCLUSION: semCDI provides a formulation for the creation of queries on the semantic representation of caBIG. This constitutes the foundation to build a semantic data integration system for more efficient and effective querying and exploratory searching of cancer-related data.
Authors: R Alonso-Calvo; V Maojo; H Billhardt; F Martin-Sanchez; M García-Remesal; D Pérez-Rey Journal: J Biomed Inform Date: 2006-03-20 Impact factor: 6.317
Authors: Jonathan Tobias; Ram Chilukuri; George A Komatsoulis; Sambit Mohanty; Nicholas Sioutos; Denise B Warzel; Lawrence W Wright; Rebecca S Crowley Journal: BMC Med Inform Decis Mak Date: 2006-06-20 Impact factor: 2.796
Authors: Helena F Deus; Diogo F Veiga; Pablo R Freire; John N Weinstein; Gordon B Mills; Jonas S Almeida Journal: J Biomed Inform Date: 2010-12 Impact factor: 6.317
Authors: Mathias Brochhausen; Martin N Fransson; Nitin V Kanaskar; Mikael Eriksson; Roxana Merino-Martinez; Roger A Hall; Loreana Norlin; Sanela Kjellqvist; Maria Hortlund; Umit Topaloglu; William R Hogan; Jan-Eric Litton Journal: J Biomed Semantics Date: 2013-10-08