Event detection from text data is an active area of research. While the emphasis in the literature has been on event identification and labeling using a single data source, this work considers event and story line detection when using a large number of data sources. In this setting, it is natural for different events in the same domain, e.g., violence, sports, politics, to occur at the same time and for different story lines about the same event to emerge. To capture events in this setting, we propose an Offline algorithm that detects events and story lines about events for a target domain given a news article collection. Our algorithm leverages a multi-relational sentence-level semantic graph and well-known graph properties to identify overlapping events and story lines within the events. We then extend this algorithm for an Online setting. Both the Offline and Online approaches are evaluated using two large data sets containing millions of news articles from a large number of sources. Our empirical analysis shows that methods using the proposed semantic graph beat the state of the art in terms of precision and recall while providing more complete event summaries.