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Mining the Web for Learning Ontologies: State of Art and Critical Review

Received: 30 March 2017     Accepted: 7 April 2017     Published: 13 May 2017
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Abstract

The aim of the paper is to investigate and present the subject of building ontologies using the Semantic Web Mining that is defined as the combination of the two fast-developing research areas Semantic Web and Web Mining.Web mining is the application of data mining techniques to the content, structure, and usage of Web resources and The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks.. This can help to discover global as well as local structure “models” or “patterns”within and between Web pages and ontology extraction witch is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between those concepts, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process. This paper gives an overview of where the two areas meet today, and discuss ways of how a closer integration could be profitable.

Published in International Journal of Sensors and Sensor Networks (Volume 5, Issue 5-1)

This article belongs to the Special Issue Smart Cities Using a Wireless Sensor Networks

DOI 10.11648/j.ijssn.s.2017050501.13
Page(s) 13-17
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Semantic Web, Web Mining, Ontology, Konwledge Discovery, Ontology Learning

References
[1] T. Berners-Lee, N. Shadbolt, and W. Hall, “The Semantic Web Revisited ” IEEE intelligent Systemes, pp. 96-101, 2006.
[2] https://www.w3.org/TR/1999/REC-rdf-syntax-19990222/.
[3] M.-S. Chen, J. Han, and P. S. Yu, Data mining: an overview from a database perspective, IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6):866-883.
[4] U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthrusamy, eds., Advances in knowledge discovery and data mining, Menlo Park, California: AAAI Press/ The MIT Press, 1996.
[5] Web-Ontology (WebOnt) Working Group, 2001, http://www.w3.org/2001/sw/WebOnt/.
[6] T. R. Gruber Toward principles for the design of ontologies used for knowledge sharing Int. J. Hum.-Comput. Stud., 43 (5) (1995), pp. 907–928.
[7] H. O. Nigro, S. G. Cisaro, D. H. Xodo Data Mining With Ontologies: Implementations, Findings and Frameworks, Information Science Reference, Imprint of: IGI Publishing, Hershey, PA (2007).
[8] Cimiano, Philipp; Völker, Johanna; Studer, Rudi (2006). "Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text", Information, Wissenschaft und Praxis, 57, p. 315 – 320.
[9] Wong, W., Liu, W. & Bennamoun, M. (2012), "Ontology Learning from Text: A. Look back and into the Future". ACM Computing Surveys, Volume 44, Issue 4, Pages 20:1-20:36.
[10] Völker, Johanna; Hitzler, Pascal; Cimiano, Philipp (2007). "Acquisition of OWL DL Axioms from Lexical Resources", Proceedings of the 4th European conference on The Semantic Web, p. 670 – 685.
[11] Thomas Wächter, Götz Fabian, Michael Schroeder: DOG4DAG: semi-automated ontology generation in OBO-Edit and Protégé. SWAT4LS London, 2011. doi:10.1145/2166896.2166926.
[12] https://www.w3.org/OWL/.
[13] Naing, M.-M, Lim, E.-P., and Chiang, R. H.-L.,“Core: A Search and Browsing Tool for Semantic Instances of Web Sites,” Asia Pacific Web Conference (APWeb’05), 2005.
Cite This Article
  • APA Style

    Mohamed El Asikri, Salahddine Krit, Hassan Chaib, Mustapha Kabrane, Hassan Ouadani, et al. (2017). Mining the Web for Learning Ontologies: State of Art and Critical Review. International Journal of Sensors and Sensor Networks, 5(5-1), 13-17. https://doi.org/10.11648/j.ijssn.s.2017050501.13

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    ACS Style

    Mohamed El Asikri; Salahddine Krit; Hassan Chaib; Mustapha Kabrane; Hassan Ouadani, et al. Mining the Web for Learning Ontologies: State of Art and Critical Review. Int. J. Sens. Sens. Netw. 2017, 5(5-1), 13-17. doi: 10.11648/j.ijssn.s.2017050501.13

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    AMA Style

    Mohamed El Asikri, Salahddine Krit, Hassan Chaib, Mustapha Kabrane, Hassan Ouadani, et al. Mining the Web for Learning Ontologies: State of Art and Critical Review. Int J Sens Sens Netw. 2017;5(5-1):13-17. doi: 10.11648/j.ijssn.s.2017050501.13

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  • @article{10.11648/j.ijssn.s.2017050501.13,
      author = {Mohamed El Asikri and Salahddine Krit and Hassan Chaib and Mustapha Kabrane and Hassan Ouadani and Khaoula Karimi and Kaouthar Bendaouad and Hicham Elbousty},
      title = {Mining the Web for Learning Ontologies: State of Art and Critical Review},
      journal = {International Journal of Sensors and Sensor Networks},
      volume = {5},
      number = {5-1},
      pages = {13-17},
      doi = {10.11648/j.ijssn.s.2017050501.13},
      url = {https://doi.org/10.11648/j.ijssn.s.2017050501.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.s.2017050501.13},
      abstract = {The aim of the paper is to investigate and present the subject of building ontologies using the Semantic Web Mining that is defined as the combination of the two fast-developing research areas Semantic Web and Web Mining.Web mining is the application of data mining techniques to the content, structure, and usage of Web resources and The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks.. This can help to discover global as well as local structure “models” or “patterns”within and between Web pages and ontology extraction witch is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between those concepts, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process. This paper gives an overview of where the two areas meet today, and discuss ways of how a closer integration could be profitable.},
     year = {2017}
    }
    

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    VL  - 5
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Author Information
  • Department Mathematics and Informatics and Management, Laboratory of Engineering Sciences and Energy, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco

  • Department Mathematics and Informatics and Management, Laboratory of Engineering Sciences and Energy, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco

  • Department Mathematics and Informatics and Management, Laboratory of Engineering Sciences and Energy, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco

  • Department Mathematics and Informatics and Management, Laboratory of Engineering Sciences and Energy, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco

  • Department Mathematics and Informatics and Management, Laboratory of Engineering Sciences and Energy, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco

  • Department Mathematics and Informatics and Management, Laboratory of Engineering Sciences and Energy, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco

  • Department Mathematics and Informatics and Management, Laboratory of Engineering Sciences and Energy, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco

  • Department Mathematics and Informatics and Management, Laboratory of Engineering Sciences and Energy, Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco

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