Recently, the use of blogs has been a remarkable means to publish user interests. In order to find suitable information resources from a large amount of blog entries which are published every day, we need an information filtering technique to automatically transcribe user interests to a user profile in detail. In this paper, we first classify user blog entries into service domain ontologies and extract interest ontologies that express a user's interests semantically as a hierarchy of classes according to interest weight by a top-down approach. Next, with a bottom-up approach, users modify their interest ontologies to update their interests in more detail. Furthermore, we propose a similarity measurement between ontologies considering the interest weight assigned to each class and instance. Then, we detect innovative blog entries that include concepts that the user has not thought about in the past based on the analysis of approximated ontologies of a user's interests. We present experimental results that demonstrate the performance of our proposed methods using a large-scale blog entries and music domain ontologies.