A Fresh Look on Knowledge Bases: Distilling Named Events from News.

Knowledge bases capture millions of entities such as people, companies or movies. However, their knowledge of named events like sports finals, political scandals, or natural disasters is fairly limited, as these are continuously emerging entities. This paper presents a method for extracting named events from news articles, reconciling them into canonicalized representation, and organizing them into fine-grained semantic classes to populate a knowledge base. Our method captures similarity measures among news articles in a multi-view attributed graph, considering textual contents, entity occurrences, and temporal ordering. For distilling canonicalized events from this raw data, we present a novel graph coarsening algorithm based on the information-theoretic principle of minimum description length. The quality of our method is experimentally demonstrated by extracting, organizing, and evaluating 25,000 events from a corpus of 300,000 heterogeneous news articles.