Back to Current Issues

A Survey on Taxonomy learning using Graph-based Approach

Diksha R. Kamble, Krishna S. Kadam ,

Affiliations
Dept. of Computer Science and Engineering, DKTE’s TEI, Ichalkaranji (An Autonomous Institute), 416115, India.
:10.22362/ijcert/2017/v4/i11/xxxx [UNDER PROCESS]


Abstract
Taxonomy learning is an important task for developing successful applications as well as knowledge obtaining, sharing and classification. The manual construction of the domain taxonomies is a time-consuming task. To reduce the time and human effort will build a new taxonomy learning approach named as TaxoFinder. TaxoFinder takes three steps to automatically build the taxonomy. First, it identifies the concepts from a domain corpus. Second, it builds CGraphs where a node represents each of such concepts and an edge represents an association between nodes. Each edge has a weight indicating the associative strength between two nodes. Lastly TaxoFinder derives the taxonomy from the graph using analytic graph algorithm. The main aim of TaxoFinder is to develop the taxonomy in such a way that it covers the overall maximum associative strengths among the concepts in the graph to build the taxonomy. In this evaluation, compare TaxoFinder with existing subsumption method and show that TaxoFinder is an effective approach and give a better result than subsumption method.


Citation
Diksha R. Kamble and Krishna S. Kadam (2017). A Survey on Taxonomy learning using Graph-based Approach. International Journal of Computer Engineering In Research Trends, 4(11), 539-542. Retrieved from http://ijcert.org/ems/ijcert_papers/V4I1113.pdf


Keywords : Taxonomy learning, ontology learning, TaxoFinder, concept taxonomy, concept graphs, similarity, associative strength

References
[1]	M.A.Hearst, “Automatic acquisition of hyponyms from large text corpora,” in Proc.14th Conf. Comput. Linguistics, 1992, vol. 2,pp. 539–545
[2]	F.M.Suchanek, G.Ifrim, and G.Weikum, “Combining linguistic and statisticalanalysis to extract relations from web documents,”in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2006, pp. 712–717.
[3]	E.-A. Dietz, D. Vandic, and F. Frasincar, “TaxoLearn: A semantic approach to domain taxonomy learning,” in Proc. IEEE/WIC/ACM Int. Joint Conf. Web Intell. Intell. Agent Technol., 2012, pp. 58–65.
[4]	W. Wang, P. Mamaani Barnaghi, and A. Bargiela,“Probabilistic topic models for learning terminological ontologies,” IEEE Trans.Knowl. Data Eng., vol. 22, no. 7, pp. 1028–1040, Jul. 2010.
[5]	Z. Kozareva and E. Hovy, “A semi-supervised method to learn and construct taxonomies using the web,” in Proc. Conf. Empirical Methods Natural Language Process., 2010, pp. 1110–1118.
[6]	P. Velardi, S. Faralli, and R. Navigli, “OntoLearn Reloaded: A graph-based algorithm for taxonomy induction,” Comput. Linguistics,vol. 39, no. 3, pp. 665–707, 2013.
[7]	K. Meijer, F. Frasincar, and F. Hogenboom, “A semantic approachfor extracting domain taxonomies from text,” Decision SupportSyst., vol. 62, pp. 78–93, 2014.
[8]	Y.-B. Kang, P. D. Haghighi, and F. Burstein, “CFinder: An Intelligent Key Concept Finder from Text for Ontology Development,”Expert Syst. Appl., vol. 41, no. 9, pp. 4494–4504, 2014.
[9]	Yong-Bin Kang, Pari Delir Haghigh, and Frada Burstein,”TaxoFinder: A graph-based approach for taxonomy learning.” Vol.28, no 2,2016.
[10]	Satish Kumar, Sujan Babu Vadde, ” Typicality Based Content-Boosted Collaborative Filtering  Recommendation Framework. “International Journal of Computer Engineering in Research Trends., vol.2, no.11, pp. 809-813, 2015.
[11]	Y.Usha Sree,P.Ragha Vardhani.” Pattern Finding in Large Datasets with Big Data Analytics Mechanism. “International Journal of Computer Engineering in Research Trends., vol.2, no.5, pp. 359-364, 2015.


DOI Link : Not yet assigned

Download :
  V4I1113.pdf


Refbacks : Currently there are no ref backs

Quick Links


DOI:10.22362/ijcert


Science Central

Score: 13.30



Submit your paper to editorijcert@gmail.com

>