Shironoshita E Patrick, Jean-Mary Yves R, Bradley Ray M, Kabuka Mansur R
INFOTECH Soft, Inc., 9200 Dadeland Blvd., Ste 620, Miami, FL 33156, USA.
J Am Med Inform Assoc. 2008 Jul-Aug;15(4):559-68. doi: 10.1197/jamia.M2732. Epub 2008 Apr 24.
To develop mechanisms to formulate queries over the semantic representation of cancer-related data services available through the cancer Biomedical Informatics Grid (caBIG).
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.
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.
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.
开发机制，以便针对通过癌症生物医学信息学网格（caBIG）提供的癌症相关数据服务的语义表示来制定查询。
semCDI查询制定将caBIG语义概念、元数据和数据的视图用作本体，并定义一种使用SPARQL查询语言（通过霍恩规则进行扩展）来指定查询的方法。semCDI能够通过建模为对象属性的关联来连接表示不同概念的数据，并通过建模为数据类型属性的公共数据元素（CDE）来合并不同源中表示相同概念的数据，使用霍恩规则来指定指示数据合并条件的附加语义。验证为了验证这种制定方法，构建了一个原型，并针对当前可用的caBIG数据服务执行了两个查询。
semCDI查询制定利用caBIG中可用的丰富语义元数据来构建查询并集成来自多个源的数据。随着更多数据服务在caBIG中注册，以及caBIG的NCI叙词表中包含的知识与数据服务中包含的数据之间能够实现更多的链接，其前景将得到进一步提升。
semCDI为基于caBIG语义表示创建查询提供了一种制定方法。这构成了构建语义数据集成系统的基础，以便更高效、有效地查询和探索癌症相关数据。
