Innovation Detection Based on User-Interest Ontology of Blog Community

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...

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Bibliographic Details
Published inLecture notes in computer science pp. 515 - 528
Main Authors Nakatsuji, Makoto, Miyoshi, Yu, Otsuka, Yoshihiro
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Class Hierarchy
Class Topology
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Domain Ontology
Exact sciences and technology
Information systems. Data bases
Memory organisation. Data processing
Recommendation List
Software
User Interest
Web community
Filtering
Ontology
Similarity
Top down method
Updating
Information extraction
Semantic web
Innovation
User service
Semantics
Music
Bottom up method
User behavior
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Summary: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.
ISBN:9783540490296
3540490299
ISSN:0302-9743
1611-3349
DOI:10.1007/11926078_37