Abstract:
Traditional web which is the largest information database lacks semantic and as a result, the information available in the web is only human understandable, not by machine. With the rapid increase in the amount of information on networks, search engine has become the infrastructure for people gaining access to Web information, and is the second largest Internet application besides e-mail. However, search engine returns a huge number of results, and the relevance between results and user queries is also different. There are lots of search engines available today, but the way to retrieve meaningful information is difficult. To overcome this problem in search engines to retrieve meaningful information intelligently or smartly, Semantic Web technology has played a major role. In the light of this, this research work, proposes an algorithm, architecture for the semantic web based search engine powered by XML meta-tags (which ensures machine understandability) to enhance web search. The model provides a simple interface to capture user’s queries (keywords), then the search or query engine processes the queries from the repository (database) using the search engine algorithm and the Resource Description Framework triple rule, interpreting the queries, retrieving and providing appropriate ranking of results in order to satisfy user’s queries. Query answers are ranked using extended information-retrieval techniques and are generated in an order of ranking. The power of XML meta-tags deployed on the web page stored in the database to search the queried information was used. The XML pages consist of user defined tags. The metadata information of the pages is stored and extracted from the database. The performance of this system as well as the precision were measured experimentally and compared to some search engines. The result showed that the developed system is fast, efficient, scalable and ranks quality results highly.
