Improved Semantic E-learning Framework for Complex Query Processing in Heterogeneous Web Services

Semantic e-learning has emerged as a next generation e-learning platform by enabling advanced manipulation, sharing, exchange, usage, and complete understanding of learning resources. Semantic framework is an appropriate methodology for extracting relevant contents frome-learning applications. However, Web-based data extraction suffers from the lack of discovery of heterogeneous Web services. Also, e-learning contents mainly focus on single domain ontologies. This paper presents an improved Semantic e-learning framework by extending or enhancing single domain ontologies to heterogeneous Web services. The proposed e-learning framework is built by integrating different and efficient matching mechanisms for handling and acquiring all kinds of heterogeneous Web-data contents and complex query data processing. The discovery of unified query processing services resulted in the extension of Web services. Our proposed framework also involves the processing of complex query in the heterogeneous e-learning framework. From the comparative analysis, it is clearly observed that the proposed framework yields higher precision, recall, and F-measure values than the existing attribute-based and item-based filtering algorithms.