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Fuzzy Concept Map Generation from Academic Data Sources

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Applications of Artificial Intelligence Techniques in Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 698))

Abstract

Increasing unstructured data in the internet by academics, digital document, and social media leads to task of mining and representing the hidden knowledge correctly. But in polysemic words, many morphologically similar terms, difficulty in human languages makes the task much difficult for the machine while humans understands and disambiguates the meaning of text by context. Text mining, being an important field of research, has many ground-level challenges because of no common platform, especially for free-flowing text or natural languages. Extracting hidden basic information in the text is clearly a major challenge. So, important topics or concepts, which user wants to learn, are mined with their fuzzy context. Taxonomies or concept hierarchies’ plays an important role in any knowledge representation system like online learning or E-learning. Application of concept extraction is creation of concept maps, a knowledge representation technique, for domains like E-learning. In this paper, we have mined the important topics or concept. We have then applied windowing process to get context vector of these concepts. We have applied Mutual Information (MI) and Balanced Mutual Information (BMI) techniques to calculate fuzzy membership values to get fuzzy context vector. The final results have reflected the fuzzy distance between concepts with the aim to implement a concept map learning system with unsupervised learning from the academic data sources.

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References

  1. P. Gärdenfors, Conceptual spaces: The Geometry of Thought (MIT press, 2004)

    Google Scholar 

  2. S. Naidu, E-learning: a guidebook of principles, procedures and practices. Commonwealth Educational Media Centre for Asia (CEMCA) (2006)

    Google Scholar 

  3. R.Y.K. Lau, D. Song, Y. Li, T.C.H. Cheung, J.-X. Hao, Toward a fuzzy domain ontology extraction method for adaptive e-learning. IEEE Trans. Knowl. Data Eng. 21(6), 800–813 (2009)

    Article  Google Scholar 

  4. C.-H. Lee, G.-G. Lee, Y. Leu, Application of automatically constructed concept map of learning to conceptual diagnosis of e-learning. Expert Syst. Appl. 36(2), 1675–1684 (2009)

    Article  Google Scholar 

  5. G. Salton, M. McGill, Introduction to Modern Information Retrieval (McGraw-Hill, 1983)

    Google Scholar 

  6. A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  7. R.Y.K. Lau, J.X. Hao, M. Tang, X. Zhou, Towards context-sensitive domain ontology extraction, in 40th Annual Hawaii International Conference on System Sciences, 2007, HICSS 2007 (IEEE, 2007), pp. 60–60

    Google Scholar 

  8. N.-S. Chen, C.-W. Wei, H.-J. Chen, Mining e-Learning domain concept map from academic articles. Comput. Educ. 50(3), 1009–1021 (2008)

    Article  Google Scholar 

  9. H. Jing, E. Tzoukermann, Information retrieval based on context distance and morphology, in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM, 1999), pp. 90–96

    Google Scholar 

  10. Y. Wu, S. Zhang, W. Zhao, Towards learning domain ontology from legacy documents, in Fourth International Conference on Digital Society, 2010, ICDS’10 (IEEE, 2010), pp. 164–171

    Google Scholar 

  11. X. Zhou, Y. Li, P. Bruza, Y. Xu, R.Y.K. Lau, Two-stage model for information filtering, in IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008, WI-IAT’08, vol. 3 (IEEE, 2008), pp. 685–689

    Google Scholar 

  12. M.-F. Moens, R. Angheluta, Concept extraction from legal cases: the use of a statistic of coincidence, in Proceedings of the 9th International Conference on Artificial Intelligence and Law (ACM, 2003), pp. 142–146

    Google Scholar 

  13. B. Fortuna, M. Grobelnik, D. Mladenić, System for semi-automatic ontology construction (2006)

    Google Scholar 

  14. A. Maedche, V. Pekar, S. Staab, Ontology learning part one—on discovering taxonomic relations from the web. Web Intell. 301–319 (2003). Springer Berlin Heidelberg

    Google Scholar 

  15. K. Englmeier, F. Murtaghµ, J. Mothe, Domain ontology: automatically extracting and structuring community language from texts. IADIS Appl. Comput. 59–66 (2007). (Spain, Espagne)

    Google Scholar 

  16. L. Drumond, R. Girardi, Extracting ontology concept hierarchies from text using markov logic, in Proceedings of the 2010 ACM Symposium on Applied Computing (ACM, 2010), pp. 1354–1358

    Google Scholar 

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Correspondence to Rafeeq Ahmed .

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Ahmed, R., Ahmad, T. (2019). Fuzzy Concept Map Generation from Academic Data Sources. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds) Applications of Artificial Intelligence Techniques in Engineering. Advances in Intelligent Systems and Computing, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-13-1819-1_39

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