  We address the Named Entity Disambiguation (NED) problem for short,
user-generated texts on the social Web. In such settings, the lack of
linguistic features and sparse lexical context result in a high degree of
ambiguity and sharp performance drops of nearly 50% in the accuracy of
conventional NED systems. We handle these challenges by developing a model of
user-interest with respect to a personal knowledge context; and Wikipedia, a
particularly well-established and reliable knowledge base, is used to
instantiate the procedure. We conduct systematic evaluations using individuals'
posts from Twitter, YouTube, and Flickr and demonstrate that our novel
technique is able to achieve substantial performance gains beyond
state-of-the-art NED methods.
