Open Access   Article Go Back

Recent Survey on Automatic Ontology Learning

R. Manimala1 , G. MuthuLakshmi2

Section:Survey Paper, Product Type: Journal Paper
Volume-07 , Issue-08 , Page no. 143-147, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si8.143147

Online published on Apr 10, 2019

Copyright © R. Manimala, G. MuthuLakshmi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: R. Manimala, G. MuthuLakshmi, “Recent Survey on Automatic Ontology Learning,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.143-147, 2019.

MLA Style Citation: R. Manimala, G. MuthuLakshmi "Recent Survey on Automatic Ontology Learning." International Journal of Computer Sciences and Engineering 07.08 (2019): 143-147.

APA Style Citation: R. Manimala, G. MuthuLakshmi, (2019). Recent Survey on Automatic Ontology Learning. International Journal of Computer Sciences and Engineering, 07(08), 143-147.

BibTex Style Citation:
@article{Manimala_2019,
author = {R. Manimala, G. MuthuLakshmi},
title = {Recent Survey on Automatic Ontology Learning},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {07},
Issue = {08},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {143-147},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=935},
doi = {https://doi.org/10.26438/ijcse/v7i8.143147}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i8.143147}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=935
TI - Recent Survey on Automatic Ontology Learning
T2 - International Journal of Computer Sciences and Engineering
AU - R. Manimala, G. MuthuLakshmi
PY - 2019
DA - 2019/04/10
PB - IJCSE, Indore, INDIA
SP - 143-147
IS - 08
VL - 07
SN - 2347-2693
ER -

           

Abstract

Semantic Web allows machine to understand the data, for that machine-readable semantic metadata is needed.Intelligence is necessary for the creation and processing of semantic metadata. Ontologies play an important role to implement the idea of the semantic web. Ontology is about the exact description of things and their relationships to represent the knowledge. Nowadays Automatic annotation based on artificial intelligence is required for gathering such knowledge. Manual ontology construction is labour-intensive, error-prone process, inflexible, expensive, time consuming and complex task. Ontology Generation or ontology learning includes the automatic extraction of domain’s terms and the relationships between the concepts from a corpus of text, and encoding them with an ontology language for easy information retrieval. Automatic Ontology generation and sharing it through the web make the web content more accessible to machine. Ontologies can be automatically extracted using various techniques. This paper describes the survey about automatic ontology extraction techniques and various methods used to extract the ontology.

Key-Words / Index Term

Ontology, Resource description Framework, ontology learning, Ontology Acquisition

References

[1] Alani, Harith; Kim, Sanghee; Millard, David E.; Weal, Mark J.; Hall, Wendy; Lewis, Paul H. and N.R. Shadbolt, “Automatic ontology-based knowledge extraction from web documents”. IEEE Intelligent Systems, 18(1) pp. 14–21. Jan-Feb 2003.
[2] Alexandra Moreira, Jugurta Lisboa Filho, and Alcione de Paiva Oliveira ,”Automatic creation of ontology using a lexical database: an application for the energy sector”, International Conference on Applications of Natural Language to Information Systems NLDB 2016: Natural Language Processing and Information Systems pp 415-420, 17 June 2016.
[3] Amel grissa touzi1, hela ben massoud and alaya ayadi,“Automatic ontology generation for data mining using FCA and clustering”, 7 Nov 2013.
[4]Andreia Dal Ponte Novelli,José Maria Parente de Oliveira, “Simple Method for Ontology Automatic Extraction from Documents” , (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 12, 2012.
[5] Caden Howell,” Machine Learning Methods of Mapping Semantic Web Ontologies”,Published in November 22, 2008.
[6] D. Heyer, M. Läuter, U. Quasthoff, T. Wittig and C. Wolff, “Learning relations using collocations”, Proceedings of the IJCAI 2001 Workshop on Ontology Learning, 2001.
[7] Mohammad Syafrullah , Naomie Salim ,” A Framework for Ontology Learning from Textual Data “,published in 2009.
[8] J. I. Toledo-Alvarado, A. Guzmán-Arenas, G. L. Martínez-Luna,” Automatic Building of an Ontology from a Corpus of Text Documents Using Data Mining Tools”, Journal of Applied Research and Technology, Vol. 10 No.3, June 2012.
[9] Jone Correia, Rosario Girardi, Carla Faria,” Extracting Ontology Hierarchies From Text”,
Conference: Proceedings of the 23rd International Conference on Software Engineering & Knowledge Engineering (SEKE`2011), Eden Roc Renaissance, Miami Beach, USA, January 2011.
[10] M. Doerr, The CIDOC Conceptual Reference Module: An Ontological Approach to Semantic Interoperability of Metadata, AI Magazine,24(3) (2003).
[11]M. Shamsfard and A. Barforoush. “The state of the art in ontology learning: A framework for comparison. The Knowledge Engineering Review”, Vol. 18 No.4, pp. 293-316, 2003.
[12]Mazen Alobaidi, Khalid Mahmood Malik and Susan Sabra. Alobaidi, “Linked open data-based framework for automatic biomedical ontology generation “, BMC Bioinformatics (2018) 19:319.
[13] MA. Hearst, “Automatic acquisition of hyponyms from large text corpora”, Proceedings of the 14th International Conference on Computational Linguistics, 539–545, 1992.
[14]Mithun Balakrishna, Munirathnam Srikanth, “Automatic Ontology Creation from Text for National Intelligence Priorities Framework (NIPF)”, Published in OIC 2008.
[15] Pease, A., Niles, I., and Li, J.,”The Suggested Upper Merged Ontology: A Large Ontology for the Semantic Web and its Applications”, in Working Notes of the AAAI-2002 Workshop on Ontologies and the Semantic Web, Edmonton, Canada, July 28-August 1, (2002).
[16]Philipp Cimiano, Andreas Hotho, Steffen Staab,” ”Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis”, Journal of Artificial Intelligence Research (JAIR) pages 305–339, 2005.
[17]Rakesh Agrawal and Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB `94), 1994, pp 487-499, San Francisco, CA, USA.
[18]R.Girardi. “Analyzing the Problem and Main Approaches for Ontology Population”, Proceedings of 10th International Conference on Information Technology: New Generations, 2013.
[19]Roberto navigli and Paola velardia,”From Glossaries to Ontologies: Extracting Semantic Structure from Textual Definitions”, Published in Ontology Learning and Population 2008.
[20]Sanju Mishra, Sarika Jain,” Automatic Ontology Acquisition and Learning”, IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308, Volume: 03 Special Issue: 14 | Nov-2014 | SMART-2014.
[21] Sidi Benslimane, Mimoun Malki, Mustapha Rahmouni2, and Adellatif Rahmoun3 ,” Towards Ontology Extraction from Data-Intensive Web Sites: An HTML Forms-Based Reverse Engineering Approach” , The International Arab Journal of Information Technology, Vol. 5, No. 1, January 2008 .
[22] Ting Wang12, Yaoyong Li1 , Kalina Bontcheva1 , Hamish Cunningham1 , and Ji Wang, “Automatic Extraction of Hierarchical Relations from Text”, Proceedings of the 3rd European conference on The Semantic Web: research and applications Pages 215-229, June 11 - 14, 2006.
[23] Trent Apted, Judy Kay,” Automatic Construction of Learning Ontologies”, Published in ICCE 2002.
[24] W. Wilson, W. Liu and M. Bennamoun, “Ontology learning from text: A look back and into the future”, ACM Comput. Surv. 44, 4, Article 20, 2012.