Approaches for Text Mining using Ontology

Authors

  • Atish M Shah

Keywords:

unstructured document, information structuring, information extraction, ontology

Abstract

Extraction of information from the unstructured document depending on an ontology application describes domain of interest which is presented as a new approach. To start with such ontology, we formulate rules to extract constants and context keywords from unstructured documents. For every unstructured document of interest, constants and keywords are extracted and a recognizer is applied to organize constants which are extracted as attribute values of tuples in a database schema generated. To make approach general, all the process is fixed and only ontological description is changed according to different application domain. In this paper, we are describing on two different types of unstructured document: firstly as offline which is based on specific PDF document and secondly as online which is Web-based and our approach attained recall scale in 80 percent and 90 percent range and accuracy near 98 percent.

Downloads

Download data is not yet available.

References

Dnyanesh G. Rajpathak, Satnam Singh, Member “An Ontology-Based Text Mining Method to Develop D-Matrix from Unstructured Text,” IEEE Transactions on System, Man and Cybernetics System, Vol.44,No.7, July 2013.

M.Schuh, J.Sheppard and C.Izurieta, “Ontology-guided knowledge discovery of event sequences in maintenance data," IEEE AUTOTESTCON Conf., vol. 7, no. 5, Mar. 2011.

M.Gaeta, F. Orciuoli and S. Salerno, “Ontology extraction for knowledge reuse: The e-learning perspective," IEEE trans.

Syst., Man, Cybern. A, Syst., Humans, vol. 41, no. 4, pp. 798{809, 2011.

AM. Schuh, J. Sheppard and C. Izurieta, “A visualization tool for knowledge discovery in maintenance event sequences," IEEE Aerosp. Electron. Syst. Mag, vol. 28, no. 7, pp. 30{39, 2013.

S. Singh and C. Pinion, “Data-driven framework for detecting anomalies in eld failure data," IEEE Aerosp. Conf., vol. 7, no. 5, Apr. 2011.

W.Zhang, T. Yoshida and Q. Wang, “Text clustering using frequent item sets,” Knowledge.-Based System, vol. 23, no. 5, pp. 379-388, 2010.

J. Sheppard, M. Kaufman, and T. Wilmering, Model based standards for diagnostic and maintenance information integration, in Proc. IEEE

AUTOTESTCON Conf., 2012, pp. 304310.

Berners-Lee, T, Hendler, J, Lassila, O.: The Semantic Web, Scientific American ; 2001.

D. C. Wimalasuriya and D. Dou. Ontology-based information extraction:

An introduction and a survey of current approaches. Journal of Information Science, 2010, 36(3): 306.

P. Cimiano. Ontology Learning and Population from Text: Algorithms,

Evaluation and Applications. Springer-Verlag New York, Inc., Secaucus,

NJ, USA, 2006. ISBN 0387306323. 15, 66, 67, 72

B. Popov, A. Kiryakov, D. Ognyano, D. Manov, and A. Kirilov. KIM

A semantic platform for information extraction and retrieval. Natural

Language Engineering, 10 (3-4):375-392, 2004. 65, 68

Alexander Maedche2, Gunter Neumann1, Steffen Staab Bootstrapping

an Ontology-Based Information Extraction System. In: Studies in Fuzziness and Soft Computing, IntelligentExploration of the Web, Springer, 2002

Additional Files

Published

10-12-2020

How to Cite

Atish M Shah. (2020). Approaches for Text Mining using Ontology. Vidhyayana - An International Multidisciplinary Peer-Reviewed E-Journal - ISSN 2454-8596, 6(3). Retrieved from https://j.vidhyayanaejournal.org/index.php/journal/article/view/339