Assigning Semantic Labels to Data Sources
published: July 15, 2015, recorded: June 2015, views: 2099
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
There is a huge demand to be able to find and integrate heterogeneous data sources, which requires mapping the attributes of a source to the concepts and relationships defined in a domain ontology. In this paper, we present a new approach to find these mappings, which we call semantic labeling. Previous approaches map each data value individually, typically by learning a model based on features extracted from the data using supervised machine-learning techniques. Our approach differs from existing approaches in that we take a holistic view of the data values corresponding to a semantic label and use techniques that treat this data collectively, which makes it possible to capture characteristic properties of the values associated with a semantic label as a whole. Our approach supports both textual and numeric data and proposes the top k semantic labels along with their associated confidence scores. Our experiments show that the approach has higher label prediction accuracy, has lower time complexity, and is more scalable than existing systems.
Download slides: eswc2015_krishnamurthy_data_sources_01.pdf (723.5 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !