Discipline-Ontology Based Learning Resources Semantic Retrieval Algorithm

Qing Yang, Jiaquan Xiao
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Abstract

There is much difference among learners and learning resource providers when the semantic of learning resources is understood and expressed, and it is the important reason which causes lower accuracy in learning resources retrieval. In order to resolve the problem above, three mechanisms are applied as follows: 1) Discipline Ontology is constructed, which is the formalization for concepts and the relationships between concepts existing in some discipline domain. OWL is adopted as Discipline Ontology description language; 2) Inference rules are defined on the basis of Discipline Ontology. Semantic extension on keyword from user is performed by using Jena inference engine and inference rules , so as to better interpret and describe the requirement; 3) Learning resource metadata is extracted and defined by following Learning Resource Meta-data Specification, so as to provide formal description for learning resources. A semantic retrieval framework for learning resources is presented, and the process of learning resource semantic retrieval algorithm is discussed in detail. Firstly, the semantic extension on inquiry keyword from user is performed on the basis of Discipline Ontology; secondly, by using the improved similarity calculating formula, the keywords produced by semantic extension are sequenced. A set of keywords which have higher similarity with inquiry keyword are sorted out, and are used as inquiry keywords; then, search is performed on the basis of inquiry keywords and learning resource metadata. A set of descriptions for learning resources, which probably meet the requirement of user, is sent to user. The algorithm provides an approach for learning resource retrieval, and is able to support the effective access on learning resources.
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基于学科本体的学习资源语义检索算法
学习者和学习资源提供者对学习资源语义的理解和表达存在较大差异,这是导致学习资源检索准确率较低的重要原因。为了解决上述问题,运用了以下三种机制:1)构建学科本体,即学科领域中存在的概念和概念间关系的形式化。采用OWL作为学科本体描述语言;2)在学科本体的基础上定义推理规则。利用Jena推理引擎和推理规则对用户关键字进行语义扩展,从而更好地解释和描述需求;3)按照《学习资源元数据规范》提取和定义学习资源元数据,为学习资源提供形式化描述。提出了一种学习资源语义检索框架,详细讨论了学习资源语义检索算法的实现过程。首先,在学科本体的基础上对用户查询关键字进行语义扩展;其次,利用改进的相似度计算公式,对语义扩展生成的关键词进行排序;整理出一组与查询关键字相似度较高的关键字,作为查询关键字;然后,根据查询关键词和学习资源元数据进行搜索。将一组可能满足用户需求的学习资源描述发送给用户。该算法为学习资源的检索提供了一种方法,能够支持对学习资源的有效访问。
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