The evalution of information retrieval technology on the web has led to the idea of semantic search engine in which it understands the meaning and the context of the search query. As a consequence,the search results returned by this type of search engine should match closely with the query.However,the web is still dominated by web 2.0 in which information and data is presented is an unstructured manner and is only fit for human consumption.Hence,building a semantic search engine is a very challenging task and there is still a lot of improvement that needs to be done to achieve the desirable results. As an example,if we search for "food that is not halal", existing semantic search engines still ignore the term of "not" resulting in inaccurate search. In view of this problems,this paper proposes a semantic meta search engine that utilizes the power of a traditional search engine (Google) and enriches the search result using DBpedia as the knowledge base to produce better results.This paper also describes the application of the knowledge base contained in DBpedia to deliver an improved search engine. Keywords: Semantic search, search engine, DBpedia.