A practical ontology query expansion algorithm for semantic-aware learning objects retrieval

  • Authors:

  • Ming-Che Lee;Kun Hua Tsai;Tzone I. Wang

  • Affiliations:

  • Laboratory of Intelligent Network Applications, Department of Engineering Science, National Cheng Kung University, No. 1, Ta-Shueh Road, Tainan, Taiwan, ROC;Laboratory of Intelligent Network Applications, Department of Engineering Science, National Cheng Kung University, No. 1, Ta-Shueh Road, Tainan, Taiwan, ROC;Laboratory of Intelligent Network Applications, Department of Engineering Science, National Cheng Kung University, No. 1, Ta-Shueh Road, Tainan, Taiwan, ROC

  • Venue:
  • Computers & Education
  • Year:
  • 2008

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Abstract

Following the rapid development of Internet, particularly web page interaction technology, distant e-learning has become increasingly realistic and popular. To solve the problems associated with sharing and reusing teaching materials in different e-learning systems, several standard formats, including SCORM, IMS, LOM, and AICC, etc., recently have been proposed by several different international organizations. SCORM LOM, namely learning object metadata, facilitates the indexing and searching of learning objects in a learning object repository through extended sharing and searching features. However, LOM suffers a weakness in terms of semantic-awareness capability. Most information retrieval systems assume that users have cognitive ability regarding their needs. However, in e-learning systems, users may have no idea of what they are looking for and the learning object metadata. This study presents an ontological approach for semantic-aware learning object retrieval. This approach has two significant novel features: a fully automatic ontology-based query expansion algorithm for inferring and aggregating user intention based on their original short query, and another ''ambiguity removal'' procedure for correcting inappropriate user query terms. This approach is sufficiently generic to be embedded to other LOM-based search mechanisms for semantic-aware learning object retrieval. Focused on digital learning material and contrasted to other traditional keyword-based search technologies, the proposed approach has experimentally demonstrated significantly improved retrieval precision and recall rate.