Multi-document semantic relation extraction for news analytics

Given the overwhelming amounts of information in our current 24/7 stream of new incoming articles, new techniques are needed to enable users to focus on just the key entities and concepts along with their relationships. Examples include news articles but also business reports and social media. The fact that relevant information may be distributed across diverse sources makes it particularly challenging to identify relevant connections. In this paper, we propose a system called MuReX to aid users in quickly discerning salient connections and facts from a set of related documents and viewing the resulting information as a graph-based visualization. Our approach involves open information extraction, followed by a careful transformation and filtering approach. We rely on integer linear programming to ensure that we retain only the most confident and compatible facts with regard to a user query, and finally apply a graph ranking approach to obtain a coherent graph that represents meaningful and salient relationships, which users may explore visually. Experimental results corroborate the effectiveness of our proposed approaches, and the local system we developed has been running for more than one year.