Semantic educational data extraction using structur-al domain relationships

  • Authors

    • Manchikatla Srikanth
    2017-12-28
    https://doi.org/10.14419/ijet.v7i1.2.9060
  • Educational Content Analysis, Web Mining, Data Extraction, Semantic Principles, Content Based Data Extraction.

  • Abstract

    In the mining industry', some of the domains are most popular and it plays a vital role in the specific area. Educational Mining and Web-Data Extraction are the two important factors play a leading role in mining industry. The main objective of the proposed system is to extract the related contents from web using semantic (relating to meaning in language or logic) principles as well as to allow the providers to dynamically generate the web pages for educational content and allow the users to search and extract the data from server based on content. The main model of this system is to illustrate the adaptive learning system. For demonstration we consider the semantic principles for Educational content over dynamic environment. This site allows the providers to create web pages related to educational content dynamically and this will be getting approved by the Administrator to live in process. Once the site is live the users can search for the exact content present into the site based on semantic principles. The proposed model is designed for dynamic web data extraction and content analysis from the extracted data due to educational principles. In the proposed system Semantic Web Extraction (SWE) procedures are highly analyzed and utilized for content manipulations. Energetic data extraction scheme for users based on educational content rather than header, title, meta tags and descriptions.

  • References

    1. [1] M. Bieliková, M. Barla, and M. Šimko, “Lightweight Semantics for the ’Wild Web’,†Proc. IADIS Int. Conf. WWW/Internet, IADIS Press, pp. xxv-xxxii, 2011.

      [2] M. Bieliková, M. Å imko, M. Barla, J. Tvarožek, M. Labaj, R. Móro, I. Srba, and J. Å evcech, “ALEF: from Application to Platform for Adaptive Collaborative Learningâ€. In Recommender Systems for Tech. Enhanced Learning, Springer, pp. 195-225, 2014. https://doi.org/10.1007/978-1-4939-0530-0_10.

      [3] P. Brusilovsky, “Adaptive Hypermedia,†User Modeling and User-Adapted Interaction, vol. 11, no. 1-2, pp. 87-110, 2001. https://doi.org/10.1023/A:1011143116306.

      [4] P. Brusilovsky, “KnowledgeTree: A Distributed Architecture for Adaptive E-learning,†Proc. 13th Int. World Wide Web Conf. Alternate Track Papers & Posters, pp. 104-113, 2004. https://doi.org/10.1145/1013367.1013386.

      [5] P. Brusilovsky, J. Knapp, and J. Gamper, “Supporting Teachers as Content Authors in Intelligent Educational Systems,†Int. J. Knowledge and Learning, vol. 2, no. 3/4, pp. 191-215, 2006. https://doi.org/10.1504/IJKL.2006.010992.

      [6] P. Brusilovsky and E. Millán, “User Models for Adaptive Hypermedia and Adaptive Educational Systems,†The Adaptive Web, LNCS 4321, Springer, pp. 3-53, 2007. https://doi.org/10.1007/978-3-540-72079-9_1.

      [7] P. Cimiano, Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer US, 2006.

      [8] P. Cimiano, A. Hotho, and S. Staab, “Comparing Conceptual, Divisive and Agglomerative Clustering for Learning Taxonomies from Text,†Proc. 16th European Conf. Artificial Intelligence (ECAI 2004), pp. 435-439, 2004.

      [9] A.I. Cristea and A. de Mooij, “Designer Adaptation in Adaptive Hypermedia Authoring,†Proc. 2003 Int. Symp. Information Technology (ITCC 2003), IEEE, pp. 444-448, 2003. https://doi.org/10.1109/ITCC.2003.1197570.

      [10] P. de Bra, D. Smits, and N. Stash, “The Design of AHA!,†Proc. 17th ACM Conf. Hypertext and Hypermedia (HYPERTEXT ‘06), ACM, pp. 133-134, 2006.

      [11] J. de Knijff, F. Frasincar, and F. Hogenboom, “Domain Taxonomy Learning from Text: The Subsumption Method versus Hierarchical Clustering,†Data & Knowledge Engineering, vol. 83, pp. 54-69, 2013. https://doi.org/10.1016/j.datak.2012.10.002.

      [12] O.L. de Lacalle and M. Lapata, “Unsupervised Relation Extraction with General Domain Knowledge,†Proc. 2013 Conf. Empirical Methods in Natural Language Processing, pp. 415-425, 2013.

      [13] D. Dicheva and C. Dichev, "Authoring educational topic maps: can we make it easier?" 5th IEEE Int. Conf. Advanced Learning Technologies, IEEE, pp. 216-218, 2005. https://doi.org/10.1109/ICALT.2005.73.

      [14] D. Dicheva and C. Dichev, “Helping Courseware Authors to Build Ontologies: The Case of TM4L,†Frontiers in Artificial Intelligence and Applications, vol. 158, IOS Press, pp. 77-84, 2007.

      [15] H.N. Fotzo and P. Gallinari, “Learning ‘Generalization/Specialization’ Relations between Concepts – Application for Automatically Building Thematic Document Hierarchies,†Proc. 7th Int. Conf. Computer-Assisted Inf. Retr., pp. 143-155, 2004.

      [16] M. Grigoriadou and K. Papanikolaou, “Authoring Personalised Interactive Content,†First International Workshop on Semantic Media Adaption and Personalization (SMAP 2006), pp. 80-85, 2006. https://doi.org/10.1109/SMAP.2006.12.

      [17] Z.S. Harris, Mathematical Structures of Language, Wiley, 1968.

      [18] J. Harinek and M. Šimko, “Improving Term Extraction by Utilizing User Annotations,†Proc. ACM Symp. Document Engineering 2013 (DocEng '13), ACM, pp. 185-188, 2013. https://doi.org/10.1145/2494266.2494306.

      [19] S. Harispe, S. Ranwez, S. Janaqi, S. and J. Montmain. “Semantic Measures for the Comparison of Units of Language, Concepts or Instances from Text and Knowledge Base Analysisâ€. arXiv preprint arXiv:1310.1285, 2013.

      [20] M. Hatala, D. Gasevic, J. Jovanovic, M. Siadaty, and C. Torniai, “Ontology Extraction Tools: An Empirical Study with Educators,†IEEE Trans. Learning Technologies, vol. 5, no. 3, pp. 275- 289, 2012, https://doi.org/10.1109/TLT.2012.9.

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  • How to Cite

    Srikanth, M. (2017). Semantic educational data extraction using structur-al domain relationships. International Journal of Engineering & Technology, 7(1.2), 175-177. https://doi.org/10.14419/ijet.v7i1.2.9060

    Received date: 2018-01-07

    Accepted date: 2018-01-07

    Published date: 2017-12-28