For example, on Live Journal1, there are a number of categories, gaming, for The explosion of popularity in social media, such as internet example, that one can categorize themselves and their forums, weblogs (blogs), wikis, etc., in the past decade blogs. While a number of those that self select that category has created a new opportunity to measure public opinion, may interact, there is no explicit requirement to do so. If attitude, and social structures (Agichtein et al. 2008, one is interested in marketing to a gaming crowd, for instance, Qualman 2010). A very common social structure investigated knowing all persons interested in gaming would be is online communities, or groups. There are a number useful, even if they do not interact directly with one another.

In this paper we introduce a system that is designed to automatically populate a knowledge base from both structured and unstructured text given an ontology. Our system is designed as a modular end-to-end system that takes structured or unstructured data as input, extracts information, maps relevant information to an ontology, and finally disambiguates entities in the knowledge base. The novelty of our approach is that it is domain independent and can easily be adapted to new ontologies and domains. Unlike most knowledge base population systems, ours includes entity detection. This feature allows one to employ very complex ontologies that include events and the entities that are involved in the events.