Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
DocBook: The Definitive Guide with CD-ROM
DocBook: The Definitive Guide with CD-ROM
Automatic document metadata extraction using support vector machines
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Metaextract: an NLP system to automatically assign metadata
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata
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Automatic Extraction of Pedagogic Metadata from Learning Content
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Web Document Classification Based on Fuzzy k-NN Algorithm
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Automated template-based metadata extraction architecture
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
Automatic metadata extraction from multilingual enterprise content
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Automatic metadata mining from multilingual enterprise content
Web Semantics: Science, Services and Agents on the World Wide Web
A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques
Information Processing and Management: an International Journal
Scientific cyberlearning resources referential metadata creation via information retrieval
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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Personalized search and browsing is increasingly vital especially for enterprises to able to reach their customers. Key challenge in supporting personalization is the need for rich metadata such as cognitive metadata about documents. As we consider size of large knowledge bases, manual annotation is not scalable and feasible. On the other hand, automatic mining of cognitive metadata is challenging since it is very difficult to understand underlying intellectual knowledge about documents automatically. To alleviate this problem, we introduce a novel metadata extraction framework, which is based on fuzzy information granulation and fuzzy inference system for automatic cognitive metadata mining. The user evaluation study shows that our approach provides reasonable precision rates for difficulty, interactivity type, and interactivity level on the examined 100 documents. In addition, proposed fuzzy inference system achieves improved results compared to a rule-based reasoner for document difficulty metadata extraction (11% improvement).