学习元平台的语义技术架构及其应用

The Architecture and Applications of Semantic Technologies in Learning Cell System

语义Web强调对网络资源附加可供机器理解和处理的语义信息,使人与电脑之间更好地协同工作,同时也使资源的大规模重用和自动化处理成为可能。近年来,随着语义Web技术的不断发展,各种e-Learning平台(适应性学习系统、智能导师系统、语义化知识社区等)纷纷尝试引入语义技术,为用户提供更加个性化和智能化的学习服务。学习元平台(LCS)基于语义化组织的学习资源,应用语义Web体系框架中的本体技术和推理技术,构建了包含Ontology API层、语义层、标注层和推理层的语义技术框架。依托该框架,LCS分别在学习资源的有序进化控制、学习资源的动态语义关联、学习资源的动态语义聚合、学习资源的适应性推荐、学习资源的语义化检索以及社会认知网络的动态构建等方面进行探索性研究,实现从资源的语义化组织到具有更强智能性和个性化的高级应用服务。这些应用实践表明,将语义技术在e-Learning系统中推广应用,需要积极引入外部开放、成熟的本体和更多高级应用服务,集成具有较强针对性和运行效率的推理机,在人工标注和自动标注相结合的基础上,关注资源和用户的语义化表征,重视情境模型的设计和情境本体的构建。

The Semantic Web places emphasis on the attached semantic information to Internet resources that can be read and processed by machine, so as to improve coordination between human and computer, and make it possible for resources to be reused and automatically processed in a large scale. With the advance of sematic web technology, it is introduced into various e-Learning platforms such as adaptive learning system, intelligent tutoring system, and semantic knowledge communities, in order to provide more personalized and intelligent learning services. Based on the semantically organized resources, by applying the ontology and reasoning technology of the semantic web system, the Learning Cell System （LCS） builds a semantic framework that contains the ontology API layer, the semantic layer, the annotation layer and the reasoning layer. Through this framework, LCS performs exploratory research in the fields of resource evolution control, dynamic semantic association and aggregation, adaptive recommendation of resources, semantic search and dynamic construction of social cognition network. It can also realize the semantic organization of resources and highly intelligent advanced application services. These practices suggest that to spread the application of semantic technology in e-learning systems, open and mature ontology and more advanced application services need to be introduced. And we should also integrate more targeted and efficient inference engines into the system, pay attention to semantic characterization of resources and users as well as the design of context models and construction of context ontology.