Semantic Web Usage Mining Research Articles

Survey on ontology Based Semantic Web Usage Mining for Enhanced Recommendation Model

Survey on ontology Based Semantic Web Usage Mining for Enhanced Recommendation Model

Optimum Session Interval based on Particle Swarm Optimization for Generating Personalized Ontology

A semantic web usage mining method is suggested to identify the association between consumer emotions and buying behaviors by utilizing the web log data. Fuzzy logic is used to signify the temporal conception and resource attributes for the requested URLS of web access activities. From this, a Personal Web usage Ontology is created which facilitates semantic web applications. But the limitation is less efficient in terms of accuracy and user satisfaction. Thus an innovative technique which is called Optimum Session Interval based Particle Swarm Optimization (OSIPSO) is introduced. This technique is used to find the optimum session interval. The Particle swarm optimization has no overlapping and mutation computation and it is proficient in global search. Additionally, an associative classification is used to enhance the accuracy. Associative classification is a combination of associative rule mining and classification rule mining. An experimental result shows that the proposed work has a high accuracy and high efficient.

Comparative Analysis of Collaborative Filtering Technique

Today it is almost impossible to retrieve information with a keyword search when the information is spread over several pages. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Web personalization is the one application of semantic web usage mining. In this report we have explored comparison of various collaborative filtering techniques. Those techniques are memory based, model based and hybrid collaborative filtering. Our study shows that the performance of hybrid collaborative filtering technique is better than memory based and model based collaborative filtering technique. We have introduced normalization step, which will improve accuracy of traditional collaborative filtering techniques.

Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis

The relationships between consumer emotions and their buying behaviors have been well documented. Technology-savvy consumers often use the web to find information on products and services before they commit to buying. We propose a semantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs which incorporates information on consumer emotions and behaviors through self-reporting and behavioral tracking. We use fuzzy logic to represent real-life temporal concepts (e.g., morning) and requested resource attributes (ontological domain concepts for the requested URLs) of periodic pattern-based web access activities. These fuzzy temporal and resource representations, which contain both behavioral and emotional cues, are incorporated into a Personal Web Usage Lattice that models the user's web access activities. From this, we generate a Personal Web Usage Ontology written in OWL, which enables semantic web applications such as personalized web resources recommendation. Finally, we demonstrate the effectiveness of our approach by presenting experimental results in the context of personalized web resources recommendation with varying degrees of emotional influence. Emotional influence has been found to contribute positively to adaptation in personalized recommendation.