Intelligent Knowledge-Point Auto-Extracting Model in Web Learning Resources

Abstract Knowledge-points auto-extracting and other Information retrieve can benefit from the use of ontologies to enrich the data with general background knowledge. The WordNet family of ontology languages was specially tailored towards such ontology-based Knowledge-points auto-extracting, enabling an implementing in data-intensive learning resources organization and management, dynamic aggregation, semantic-level share, accurate search. In this paper, we propose an approach to implementing ontology-based data access in WordNet with the distinguishing feature of optimizing density-based clustering OPTICS algorithm (DBCO) to auto-extract knowledge-point. We show that, in contrast to the existing approaches, no exponential blowup is produced by the DBCO. Based on experiments with a number of real-world data sets of 227 users in four study sites, we demonstrate that knowledge-point auto-extracting execution in the proposed approach is often efficient, especially for large-scale web learning resource. According to the user ratings data of four learning sites in the 120 days, the average rate of increase of user rating after the system is used reaches to 23.23%.