学习资源的语义众包标注系统设计

The Annotation of Learning Resources Based on Crowdsourcing

语义标注是Web3.0环境下学习资源组织方式的基础和前提。目前学习资源的语义标注多是通过本体对文档的实体进行识别和标注，一般揭示的是显性的、有确定规则的知识，对人文性学科中蕴含情境性、文化性特征可多元解释的隐性知识揭示不足，针对目前这个问题，该文提出基于众包的学习资源语义标记的思路，充分利用人的集体智慧，解决海量学习资源的复杂多元语义标注问题。研究以学习元平台为支撑，构建基于众包的语义标注系统，通过社会信任计算和关联词汇判别过滤机制进行质量控制，实现学习资源中隐性知识的标注，通过实验证明基于众包能够有效解决人文性学科中的隐性知识标注。

Semantic annotation is the foundation and precondition of learning resources organization which based on Web3.0. But there are little paper Focus on the annotation of tacit knowledge. In this paper， we put forward a new way to solve this problem by crowdsourcing which can make full use of human computation to work out the magnanimity learning resources’ tacit knowledge. further on ，we created a crowdsourcing based semantic tagging system based on LCS， via vote by the user and the machine for quality control， realized tacit knowledge during the learning resources by the above work and to prove based on crowdsourcing can effectively improve the quality of the annotation of learning resources by experiment.