Intelligent Information Processing IV: 5th IFIP by Phillip C-Y. Sheu (auth.), Zhongzhi Shi, E. Mercier-Laurent,

By Phillip C-Y. Sheu (auth.), Zhongzhi Shi, E. Mercier-Laurent, D. Leake (eds.)

International Federation for info Processing

The IFIP sequence publishes cutting-edge leads to the sciences and applied sciences of knowledge and communique. The scope of the sequence contains: foundations of machine technological know-how; software program thought and perform; schooling; desktop purposes in expertise; conversation platforms; platforms modeling and optimization; info structures; pcs and society; computers know-how; safeguard and security in info processing structures; man made intelligence; and human-computer interplay. court cases and post-proceedings of refereed overseas meetings in machine technology and interdisciplinary fields are featured. those effects frequently precede magazine e-book and signify the most up-tp-date study. The valuable goal of the IFIP sequence is to motivate schooling and the dissemination and alternate of data approximately all features of computing.

For additional info concerning the three hundred different books within the IFIP sequence, please stopover at www.springer.com.

For additional info approximately IFIP, please stopover at www.ifip.org.

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Additional resources for Intelligent Information Processing IV: 5th IFIP International Conference on Intelligent Information Processing, October 19-22, 2008, Beijing, China

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Automatic transcription of the voice file into the text file by Dragon 7 , 3. Extraction of concepts and relations between concepts 4. Validation of the extracted concepts found in the previous stage by expert. The validated descriptors are registered in a data base and will be used to update the existing ontologies. The acquired voice information is distributed in fields of the data base such as: DENOMINATION, CATEGORY, MATERIAL, DESCRIPTION, and INSCRIPTION without constraining the speaker to say the name of the descriptive field.

1994. 7. A. F. C. Garcia, Ontological Engineering: With Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web. Springer ISBN:1-85253-55j-3, 2001. 8. A. , Evaluation of Taxonomic Knowledge on Ontologies and KnowledgeBased Systems. Intl. , 1999. 9. C. Jelmini, and S. M-Maillet, OWL-based reasoning with retractable inference”, In RIAO Conference Proceedings 2004. 10. A. Maedche and S. Staab, Measuring similarity betwe- en ontologies. Proc. CIKM 2002. LNAI vol. 2473, 2002.

2. J. S. Seipel, Owls–Design Anomalies in Ontologies, 18th Intl. Floida Artificial Intelligence Research Society Conference (FLAIRS), pp 251-220, 2005. 3. C. Brewster et al, Data driven ontology evaluation. Proceedings of Intl. Conf. on Language Resources and Evaluation, Lisbon, 2004. 4. M. A. W. Noshairwan, N. Iftikhar,. DKP-OM: A Semantic Based Ontology Merger. In Proc. UCS). 2007a Evaluation of Ontologies and DL Reasoners 27 5. M. A. Qadir, W. Noshairwan, Semantic Inconsistency Errors in Ontologies.

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