  Advancements in technology and culture lead to changes in our language. These
changes create a gap between the language known by users and the language
stored in digital archives. It affects user's possibility to firstly find
content and secondly interpret that content. In previous work we introduced our
approach for Named Entity Evolution Recognition~(NEER) in newspaper
collections. Lately, increasing efforts in Web preservation lead to increased
availability of Web archives covering longer time spans. However, language on
the Web is more dynamic than in traditional media and many of the basic
assumptions from the newspaper domain do not hold for Web data. In this paper
we discuss the limitations of existing methodology for NEER. We approach these
by adapting an existing NEER method to work on noisy data like the Web and the
Blogosphere in particular. We develop novel filters that reduce the noise and
make use of Semantic Web resources to obtain more information about terms. Our
evaluation shows the potentials of the proposed approach.
