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 |
Semantic Web, Web Mining, Ontology, Konwledge Discovery, Ontology Learning
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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
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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Download@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}
}
TY - JOUR T1 - Mining the Web for Learning Ontologies: State of Art and Critical Review AU - Mohamed El Asikri AU - Salahddine Krit AU - Hassan Chaib AU - Mustapha Kabrane AU - Hassan Ouadani AU - Khaoula Karimi AU - Kaouthar Bendaouad AU - Hicham Elbousty Y1 - 2017/05/13 PY - 2017 N1 - https://doi.org/10.11648/j.ijssn.s.2017050501.13 DO - 10.11648/j.ijssn.s.2017050501.13 T2 - International Journal of Sensors and Sensor Networks JF - International Journal of Sensors and Sensor Networks JO - International Journal of Sensors and Sensor Networks SP - 13 EP - 17 PB - Science Publishing Group SN - 2329-1788 UR - https://doi.org/10.11648/j.ijssn.s.2017050501.13 AB - 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. VL - 5 IS - 5-1 ER -
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