基于语义Web语言的自适应学习系统知识集成框架

Feng-Hsu Wang, Dai Chen
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引用次数: 13

摘要

自适应学习(AL)系统一直是基于网络的个性化学习的解决方案之一。本文提出了一种基于语义Web语言的知识资源集成框架。因此,人工智能系统的知识模块可以在Internet上共享和重用,从而形成一种基于服务的方法来开发分布式人工智能系统。在此基础上,实现了一个人工智能系统原型,演示了如何开发和集成人工智能系统的知识模块。最后,初步的原型评估结果表明,在基于当前web服务实现的轻到中等请求流量下,基于服务的方法的性能是可以接受的。
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A Knowledge Integration Framework for Adaptive Learning Systems Based on Semantic Web Languages
Adaptive learning (AL) systems have long been one of the promising solutions to web-based personalized learning. This paper proposes a framework to solve the problem of integrating knowledge resources on the Web based on Semantic Web languages. As a consequence, knowledge modules of an AL system can be shared and reused on the Internet, resulting a service-based approach to developing distributed AL systems. Based on this framework, a prototype AL system was implemented to demonstrate how the knowledge modules of an AL system can be developed and integrated. Finally, a preliminary prototype evaluation result shows that the performance of the service-based approach is acceptable under light to middle traffics of requests based on current web service implementations.
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