Performance of Web Information Gathering System using TREC Model, Category Model, Web Model and Ontology Model
K. Leela Lakshmi Devi, P. Radhakrishna, Kurapati Parimala · 2012
In this paper, an ontology model is proposed for representing user background knowledge for personalized web information gathering. The model constructs user personalized ontologies by extracting world knowledge from the LCSH system and discovering user background knowledge from user local instance repositories. A multidimensional ontology mining method, exhaustively and specificity, is also introduced for user background knowledge discovery. In evaluation, the standard topics and a large tested were used for experiments. The model was compared against benchmark models by applying it to a common system for Information gathering. The experiment results demonstrate that our proposed model is promising. A sensitivity analysis was also conducted for the ontology model. In this investigation, we found that the combination of global and local knowledge works better than using any one of them. In addition, the ontology model using knowledge with both is-a and part-of semantic relations works better than using. Only one of them. When using only global knowledge, these two kinds of relations have the same Contributions to the performance of the ontology model. The proposed ontology model in this paper provides a solution to emphasizing global and local knowledge in a single computational model. The findings in this paper can be applied to the design of web information gathering systems. The model also has extensive contributions to the fields of Information Retrieval, web Intelligence, Recommendation Systems, and Information Systems.