A Semantic-based Variables Selection for Ontology Learning Taking Jaccard Alignment as Case

Abstract In the past decade, research on numerical schemes on ontology learning has been quite intensive. Several learning approaches have been proposed to help developers during the maintenance process. Most of the proposed approaches do not process the curse of dimensionality and the semantic contained in the information structure. A novel semantic-based method for ontology learning, which can provide improvement in both alignment and learning, is described. Good comparisons with the experimental studies demonstrate the multidisciplinary applications of our approach.

[1]  Truncated singular value decomposition for semantic-based data retrieval , 2013, 2013 Third International Conference on Communications and Information Technology (ICCIT).

[2]  Ichiro Sakata,et al.  How to measure the semantic similarities between scientific papers and patents in order to discover uncommercialized research fronts: A case study of solar cells , 2010, PICMET 2010 TECHNOLOGY MANAGEMENT FOR GLOBAL ECONOMIC GROWTH.

[3]  Nai-Kuan Chou,et al.  ECG data compression using truncated singular value decomposition , 2001, IEEE Trans. Inf. Technol. Biomed..

[4]  Steffen Staab,et al.  Ontology Learning for the Semantic Web , 2002, IEEE Intell. Syst..

[5]  Yanfen Shen,et al.  A formal ontology for data mining : principles, design, and evolution , 2007 .

[6]  Choukri Djellali,et al.  Enhancing text clustering model based on Truncated Singular Value Decomposition, fuzzy ART and Cross Validation , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[7]  Pascal Matsakis,et al.  Evaluation of stop word lists in text retrieval using Latent Semantic Indexing , 2011, 2011 Sixth International Conference on Digital Information Management.

[8]  José Maria Parente de Oliveira,et al.  Conceptual Maps as the First Step in an Ontology Construction Method , 2010, 2010 14th IEEE International Enterprise Distributed Object Computing Conference Workshops.

[9]  Yanhui Lv.  An approach to ontologies integration , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[10]  Andrea Bellandi,et al.  Ontology-Driven Relation Extraction by Pattern Discovery , 2010, 2010 Second International Conference on Information, Process, and Knowledge Management.

[11]  Volker Haarslev,et al.  The RacerPro knowledge representation and reasoning system , 2012, Semantic Web.

[12]  Biju Issac,et al.  Implementing spam detection using Bayesian and Porter Stemmer keyword stripping approaches , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.

[13]  Liu Ning,et al.  Using Formal Concept Analysis for Maritime Ontology Building , 2010, 2010 International Forum on Information Technology and Applications.

[14]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[15]  Samhaa R. El-Beltagy,et al.  A Survey of Ontology Learning Approaches , 2011 .

[16]  Wen-Yang Lin,et al.  Incrememtal Maintenance of Ontology-Exploiting Association Rules , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[17]  Yoshifumi Nishio,et al.  Fuzzy Adaptive Resonance Theory Combining Overlapped Category in consideration of connections , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).