Abstract
An increasingly large amount of financial information available in a number of heterogeneous business sources implies that the traditional methods of analysis are no longer applicable. These financial data sources are characterized by the use of disparate data models and their unstructured content with implicit knowledge. In addition, the most up-to-date financial information typically resides in the vast amount of financial-related news that brokers take into account when investing. As Semantic Technologies mature, they provide a consistent and reliable basis for the development of superior, more precise mechanisms to deal with heterogeneous data. In this paper, we present a financial news semantic search engine based on Semantic Web technologies. The search engine is accompanied by an ontology population tool that assists in keeping the financial ontology up-to-date. In addition, a further module has been developed that is capable of crawling the Web in search of financial news and annotating it with knowledge entities from the financial ontology that match with the contents of the news. Our contribution is an overall solution based on a fully fledged architecture that has been validated in a use case scenario for the Spanish stock exchange.
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