Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
User profiling in personal information agents: a survey
The Knowledge Engineering Review
Detecting innovative topics based on user-interest ontology
Web Semantics: Science, Services and Agents on the World Wide Web
Incremental Personalised Summarisation with Novelty Detection
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Towards knowledge extraction from weblogs and rule-based semantic querying
RuleML'07 Proceedings of the 2007 international conference on Advances in rule interchange and applications
Collaborative filtering by analyzing dynamic user interests modeled by taxonomy
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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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.