Processing of Complex Stock Market Events Using Background Knowledge

Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from data fusion of live event streams and background knowledge about companies which are stored in a knowledge base. Users of our system can express their queries in a rule language which provides functionalities to specify semantic queries about companies in the RDF SPARQL language for querying the external knowledge base and combine it with event data streams. Background makes it possible to detect stock market events based on companies attributes and not only based on syntactic processing of stock price and volume.