Abstract

There are many news articles about events reported on the Web daily, and people are getting more and more used to reading news articles online to know and understand what events happened. For an event (which may consist of several component events, i.e., episodes), people are often interested in the whole picture of its evolution and development along a time line. This calls for modeling the dependency relationships between component events. Further, people may also be interested in component events which play important roles in the event evolution or development. To satisfy the user needs in finding and understanding the whole picture of an event effectively and efficiently, we formalize the problem of temporal event search and propose a framework of event relationship analysis for search events based on user queries. We define three kinds of event relationships which are temporal relationship, content dependence relationship, and event reference relationship for identifying to what an extent a component event is dependent on another component event in the evolution of a target event (i.e., query event). The search results are organized as a temporal event map (TEM) serving as the whole picture about an event's evolution or development by showing the dependence relationships between events. Based on the event relationships in TEM, we further propose a method to measure event importance degrees so as to discover the important component events for a query. Experiments conducted on a real data set show that our method outperforms a number of baseline methods, and it can help discover certain new relationships missed by previous methods and even human annotators.