Research on Similarity Fusion in Knowledge Fusion of Learning Object Repository based on Fuzzy Set Theory

Tao Huang, Yun-Le Chen, Hao Zhang
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Abstract

The construction of learning object repository is the key infrastructure of learning object organization under the E-learning environment. At present, the learning object repository is redundant construction, scattered and confused, lack the creation of tacit knowledge into explicit knowledge, is not conducive to knowledge sharing. In view of the above problems, this paper proposed a learning object repository fusion method, designed and constructed learning object knowledge units and fusion rule base, and applied fuzzy set theory to knowledge unit similarity fusion. Finally, we verified the effectiveness and feasibility of the method through the experiments, and get the more reliable results than single knowledge source detection, reduced the uncertainty of the fusion results. It has a certain reference value for improving the quality of learning object repository and collaborative work among repositories.
基于模糊集理论的学习对象知识库知识融合中的相似度融合研究
学习对象库的构建是E-learning环境下学习对象组织的关键基础设施。目前,学习对象库存在冗余构建,分散混乱,缺乏将隐性知识转化为显性知识的创造,不利于知识共享。针对上述问题,本文提出了一种学习对象知识库融合方法,设计并构建了学习对象知识单元和融合规则库,并将模糊集理论应用于知识单元相似度融合。最后,通过实验验证了该方法的有效性和可行性,得到了比单一知识源检测更可靠的结果,降低了融合结果的不确定性。对提高学习对象库的质量和库之间的协同工作具有一定的参考价值。
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