面向应用OCR和语义网实现最佳学习体验

Kiran Badwaik, Khalid Mahmood, Asif Raza
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引用次数: 5

摘要

随着越来越多的学习者选择在线学习,在线学习行业正致力于通过提供相关内容和大量额外参考来改善在线用户的学习体验。由于在线学习者大多更喜欢视频教程,因此确定视频教程中涵盖的主要主题和次要主题是一个很大的挑战。近年来,为了有效地实现网络上的知识共享和互操作性,语义网受到了广泛的关注。在本文中,我们提出了一个基于web的语义框架,用于视频教程的自动主题识别,以识别概念及其相关的语义相关资源。我们的框架使用消除歧义的电子学习资源来识别相关主题,帮助学习者更专注地学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards applying OCR and Semantic Web to achieve optimal learning experience
As more and more learners are opting for onlinelearning, e-learning industry is working on improving learningexperience of online user by providing relevant content and lotof additional references. Since online learners mostly prefervideo tutorials, identifying major topics and subtopics coveredin video tutorial is a big challenge. Recently, for efficientknowledge sharing and interoperability over web lot ofattention is given to semantic web. In this paper, we propose asemantic web-based framework for automatic topicidentification from video tutorials in order to identify theconcepts and their associated semantically relevant resources. Our framework identifies relevant topic using disambiguationin e-learning resource which helps learners in more focused study.
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