Financial and economic data management using Semantic Web technologies

Summary form only given. In the domains of Finance and Economics, interacting with large amounts of data from heterogeneous sources is a common and critical task for both academic researchers and industrial practitioners. As the "Big Data" trend is sweeping through academia, enterprises and governments with large amounts of data from various new sources, it results in tremendous potential opportunities to extract values from it, such as automatically discovering novel patterns which would be impossible with small samples from a single data source. Meanwhile, complicated by the connectivity and interdependence of the world's markets, corporations and financial instruments, significant challenges have been posted for efficiently managing relevant datasets which would be used to facilitate scientific discoveries and support business decisions. This tutorial demonstrates an approach to address the data interchange challenges by illustrating how the Semantic Web technologies can be used in interacting with financial and economic data. We first introduce an incremental data organization model based on the Resource Description Framework (RDF), and then show the processes of data collecting, adding structures as well as domain knowledge and linking across different data sources. In keeping with CIFEr's practical spirit, this tutorial gives participants hand-on experience of using SPARQL to query large datasets in RDF, and analyzing the results with R through examples such as retrieving textual data from the New York Times and studying corporations' lobbying behaviors.