An Ontology-based Ranking Model in Search Engines

Abstract

As the tsunami of data has emerged, search engines have become the most powerful tool for obtaining scattered information on the internet. The traditional search engines return the organized results by using ranking algorithm such as term frequency, link analysis (PageRank algorithm and HITS algorithm) etc. However, these algorithms must combine the keyword frequency to determine the relevance between user’s query and the data in the computer system or internet. Moreover, we expect the search engines could understand users’ searching by content meanings rather than literal strings. Semantic Web is an intelligent network and it could understand human’s language more semantically and make the communication easier between human and computers. But, the current technology for the semantic search is hard to apply. Because some meta data should be annotated to each web pages, then the search engine will have the ability to understand the users intend. However, annotate every web page is very time-consuming and leads to inefficiency. So, this study designed an ontology-based approach to improve the current traditional keyword-based search and emulate the effects of semantic search. And let the search engine can understand users more semantically when it gets the knowledge.