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Taxofinder With Optimal Number of Concepts and Word2vector for Efficient Taxonomy Learning
Objective: To find optimal number of concepts for taxofinder to learn taxonomy by using an efficient technique. The word2vector representation is presented to define the relationship among the concepts that improves the efficiency of taxonomy learning.
Methods: There are several approaches were developed for taxonomy learning. Taxofinder is an approach that learns taxonomy based on graph representation. In this approach the concepts in text corpus were extracted and the concepts were represented in graph representation to define the associative strength between the concepts. Thus fixed number of concepts was given as input to the taxofinder to learn taxonomy it degrades the performance of taxofinder.
Findings: Taxonomies represent the relation among the concepts within a domain. Taxofinder is an approach that learns taxonomy based on graph representation. In this approach the concepts in text corpus were extracted and the concepts were represented in graph representation to define the associative strength between the concepts. Thus fixed number of concepts was given as input to the taxofinder to learn taxonomy it degrades the performance of taxofinder. In this paper, an optimal number of concepts are determined and it is fed as input to the taxofinder. Then the optimal concepts are ranked and build CGraph based on optimal number of concepts and associative strength between the concepts. The associative strength is determined by using Word2vector model.
Application/Improvements: To increase the precision, recall and f-measure for taxonomy construction taxofinder with optimal number of concepts and word2vector is proposed.
Keywords
- T. Fountain, M. Lapata. Taxonomy induction using hierarchical random graphs. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies .2012, 466-476.
- Y. B. Kang, P. D. Haghigh, F. Burstein. TaxoFinder: A Graph-Based Approach for Taxonomy Learning. IEEE Transactions on Knowledge and Data Engineering, 2016; 28(2), 524-536.
- V. U. Rani, U. E. Perumal, S. Palanivel. Morphology and taxonomy of OscillatoriaprincepsVaucher ex gomont (OSCILLATORIALES, OSCILLATORIACEAE). Indian Journal of Education and Information Management. 2016; 5(1), 1-5.
- G. Durand, N. Belacel, F. LaPlante. Graph theory based model for learning path recommendation. Information Sciences, 2013; 251, 10-21.
- Y. Han, F. Wu, J. Shao, Q. Tian, Y. Zhuang. Graph-guided sparse reconstruction for region tagging. In Computer vision and pattern recognition (CVPR), 2012 IEEE Conference on 2012 June, 2981-2988.
- A. Kalou, G. Solomou, C. Pierrakeas, A. Kameas. An ontology model for building, classifying and using learning outcomes. In 2012 IEEE 12th International Conference on Advanced Learning Technologies. 2012 July, 61-65.
- E. A. Dietz, D. Vandic, F. Frasincar. Taxolearn: A semantic approach to domain taxonomy learning. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on. 2012; 1, 58-65.
- P. D. Haghighi, F. Burstein, A. Zaslavsky, P. Arbon. Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decision Support Systems, 2013; 54(2), 1192-1204.
- M. Karpova, V. Shmelev, A Dukhanov. An automation of the course design with use of learning objects with evaluation based on the Bloom taxonomy. In Application of Information and Communication Technologies (AICT), 2015 9th International Conference onIEEE. 2015, October; 138-142.
- J. Zheng, Z. Jiang. Tag taxonomy aware dictionary learning for region tagging. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013; 369-376.
- Y. B. Kang, P. D.Haghighi, F. Burstein. CFinder: An intelligent key concept finder from text for ontology development. Expert Systems with Applications, 2014; 41(9), 4494-4504.
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