bacon: Linked Data Integration based on the RDF Data Cube Vocabulary

Abstract

Discovering and integrating relevant real-live datasets are essential tasks, when it comes to handling Linked Data. Similar to Data Warehousing approaches, Linked Data can be prepared to enable sophisticated data analysis. The developed open source framework bacon enables interactive and crowed-sourced Data Integration on Linked Data (Linked Data Integration), utilizing the RDF Data Cube Vocabulary and the semantic properties of Linked Open Data. Discovering suitable datasets on-the-fly in local or remote repositories sets up the ensuing integration process. Based on well-known Data Warehousing processes, the semantic nature of the data is taken into account to handle and merge RDF Data Cubes. To do so, structure and content of the cubes must be analyzed and processed. A similarity measure has been developed to find similarly structured cubes. The user is offered a graphical interface, where he can search for suitable cubes and modify their structure based on semantic properties. This process is fostered by a set of automated suggestions to support inexperienced users and also domain experts.