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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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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DOI: https://doi.org/10.1007/978-981-13-1819-1_39
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