In a preprocessing step, a named entity recognizer (i.e., Stanford Parser) is employed to derive mentions from a given document. Then, for each mention, a list of promising candidates is derived from Wikepdia by computing the probability of a Wikipedia article e being referred to by the mention m (i.e., P(e|m)). BEL uses the local context of a mention by operating on a textual range of relevant terms surrounding the mention. Multiple subsets are generated by randomly drawing terms from the relevant range based on bootstrapping. For each candidate entity, a statistical language model is applied on each random subset calculate the contextual similarity score and generates a ranking of the candidates based on the context captured by the subset. Each ranking classifier combines the contextual similarity score and the probability of a candidate being referred to by the mention in question. The combined score yields the final ranking of each classifier. If the majority of the ranking classifiers has the same candidate as top-ranked entity, the mention is linked to that candidate. Otherwise, we consider that the corresponding entity is not the knowledge base.