Melike Sah
ABSTRACT
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).
- Jamison, N. 2010. Beyond the Customer Satisfaction Horizon Fostering Loyalty through Customer Service. http://www.jamison-consulting.com/pdf/BeyondtheCustomer SatisfactionHorizon011210.pdfGoogle Scholar
- Khankasikam, K. (2010). A Hybrid Case-based and Rule-based for Metadata Extraction on Heterogeneous Thai Documents. In Proceedings of IEEE International Conference on Computer and Automation Engineering.Google Scholar
- Flynn, P., Zhou, L., Maly, K., Zeil, S. and Zubair, M. (2007). Automated Template-Based Metadata Extraction Architecture. ICADL. LNCS, Vol. 4822, 327--336. Google ScholarDigital Library
- Han, H., and Lee Giles, C., Manavoglu, E., and Zha, H., Zhang, Z., and Fox, E. A. (2003). Automatic Document Metadata Extraction Using Support Vector Machines. In ACM/IEEE Conference on Digital libraries, 37--48. Google ScholarDigital Library
- Zhang, J., Niu, Y., Nie, H. (2009). Web Document Classification Based on Fuzzy k-NN Algorithm. In Proceedings of International Conference on Computational Intelligence and Security, 193--196 Google ScholarDigital Library
- Sah, M. and Wade, V. 2010. Automatic Metadata Extraction from Multilingual Enterprise Content. In Proceedings of International Conference on Information and Knowledge Management (CIKM), 1665--1668. Google ScholarDigital Library
- Roy, D., Sarkar, S. and Ghose, S. (2008). Automatic Extraction of Pedagogic Metadata from Learning Content. International Journal of Artificial Intelligence in Education, 97--118. Google ScholarDigital Library
- Jovanovic, J. Gasevic, D., and Devedzic, V. (2006). Ontology Based Automatic Annotation of Learning Content. International Journal on Semantic Web and Information Systems, Vol. 2, No. 2, 91--119.Google ScholarCross Ref
- Yilmazel, O., Finneran, C. M., and Liddy, E. D. (2004). MetaExtract: An NLP System to Automatically Assign Metadata. In Proceedings of ACM/IEEE Conference on Digital Libraries, 241--242. Google ScholarDigital Library
- Flesch, R. (1948). A new readability yardstick, Journal of Applied Psychology, Vol. 32, 221--233.Google ScholarCross Ref
- Foltz, P.W., Kintsch W., and Landauer T.K. (1998). The measurement of textual coherence with latent semantic analysis. Discourse Processes, Vol. 25, No. 2, 285--307.Google ScholarCross Ref
- Ceravolo, P., Nocerino, M.C., and Viviani, M. (2004). Knowledge Extraction from Semi-Structured Data Based on Fuzzy Techniques. In International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, LNAI, Vol. 3215, 328--334.Google ScholarCross Ref
- Ceravolo, P., Damiani, E., and Viviani, M. (2007). Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata. IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 2, 149--163. Google ScholarDigital Library
- Cui, Z. Damiani, E., Leida, M., and Viviani, M. (2005). OntoExtractor: A Fuzzy-Based Approach in Clustering Semi-structured Data Sources and Metadata Generation. In Proceedings of International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, LNAI, Vol. 3681, 112--118. Google ScholarDigital Library
- Walsh, N. and Muellner, L. (1999). The DocBook Definitive Guide, O'Reilly Media. Google ScholarDigital Library
- Gueye, B., Rigaux, P., and Spyratos, N. (2004). Taxonomy-Based Annotation of XML Documents: Application to elearning Resources. In Proceedings of SETN, LNAI, Vol. 3025, 33--42.Google Scholar
- Martinez-Ortiz, I., Moreno-Ger, P., Sierra-Rodriguez, J.L. and Fernandez-Manjon, B. (2006). Using DocBook and XML Technologies to Create Adaptive Learning Content in Technical Domains. International Journal of Computer Science and Applications, Vol. 3, No. 2, 91--108.Google Scholar
- Steichen, B., O'Connor, A., and Wade, V. (2011). Personalisation in the Wild -- Providing Personalisation across Semantic, Social and Open-Web Resources. ACM Conference on Hypertext and Hypermedia. Google ScholarDigital Library
- IEEE Learning Object Model. (2002). http://ltsc. ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdfGoogle Scholar
- Zadeh, L. A. (1965). Fuzzy sets. Information and Control, Vol. 8, 338--353.Google ScholarCross Ref
- Zadeh, L. A. (1997). Towards a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems. Vol. 90, 11--127. Google ScholarDigital Library
Index Terms
- Automatic mining of cognitive metadata using fuzzy inference
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