Exploiting Background Information in Knowledge Discovery from Text
Journal of Intelligent Information Systems
Ontology-based extraction and structuring of information from data-rich unstructured documents
Proceedings of the seventh international conference on Information and knowledge management
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Proceedings of the 20th international conference on World wide web
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Proceedings of the 20th ACM international conference on Information and knowledge management
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SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the sixth ACM international conference on Web search and data mining
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The last decade has seen the rise of large knowledge bases, such as YAGO, DBpedia, Freebase, or NELL. In this paper, we show how this structured knowledge can help understand and mine trends in unstructured data. By combining YAGO with the archive of the French newspaper Le Monde, we can conduct analyses that would not be possible with word frequency statistics alone. We find indications about the increasing role that women play in politics, about the impact that the city of birth can have on a person's career, or about the average age of famous people in different professions.