An intelligent inventive system for personalised webpage recommendation based on ontology semantics

Abstract

Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users' web usage data. An overall accuracy of 87.73% is achieved by the proposed approach.

Discrete Dynamic Modeling of Learner Behavior Analysis in Physical Education Teaching

“…British universities began to monitor and study students' learning process at the end of the nineteenth century. At that time, the technical conditions were poor, and the analysis did not achieve good results (Deepak et al, 2019) [ 14 ]. With the rapid development of the information age, British education researchers use data mining and dynamic modeling technology to obtain and process students' learning data.…”