Welcome to NewsReader: “Building structured event Indexes of large volumes of financial and economic Data for Decision Making”

The volume of news data is enormous and expanding, covering billions of archived documents with millions of documents added daily. These documents are also getting more and more interconnected with knowledge from other sources such as biographies and company databases.

Professional decision makers who need to respond quickly to new developments or who need to explain these developments on the basis of the past are faced with the problem that current solutions for consulting these archives no longer work. There are simply too many possibly relevant and partially overlapping documents and from these documents decision makers still need to distinguish the correct from the wrong, the new from the old, the actual from the out-of-date by reading the content and maintaining a record in memory. Consequently, it becomes almost impossible to make well-informed decisions and professionals risk to be held liable for decisions based on incomplete, inaccurate and out-of-date information.

NewsReader will process news in 4 different languages when it comes in. It will extract what happened to whom, when and where, removing duplication, complementing information, registering inconsistencies and keeping track of the original sources. Any new information is integrated with the past, distinguishing the new from the old in an unfolding story line, similar to how people tend to remember the past and access knowledge and information. The difference here is that NewsReader can provide access to all original sources and will not forget any details (like a “History Recorder”). We will develop a decision-support tool that allows professional decision makers to explore these story lines using visual interfaces and interactions to exploit their explanatory power and their systematic structural implications. Likewise, NewsReader can make predictions from the past on future events or explain new events and developments through the past.