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Abstract and References |
Transactions on Science and Technology Vol. 4, No. 3, 232 - 251, 2017 |
DBPedia Based Meta Search Engine |
Kim Soon Gan, Kim On Chin, Patricia Anthony, Vooi Keong Boo |
ABSTRACT Retrieving information on the web has become more challenging due to the overwhelming growth of online data. Consequently, even a good ranking algorithm is not able to deliver a precise search result. Traditional search engines work based on keyword search rather than the meaning of the keyword being searched. The main problem with keyword search is that it cannot solve the synonym and polysemy problems. Semantic search engines address this problem by focusing on the meaning of the query rather than considering it as a mere search phrase (keywords). In this work, we propose MEOW, a semantic meta search engine that utilises the power of a traditional search engine and enriches the search results by trying to understand the meaning of the search query. The proposed meta search engine uses DBPedia as its knowledge base and Google to generate the search results. The search results classify which semantic concept for a given query is used for different context. The main idea of this work is to transform non-semantic search results into semantic results by extending the search query to get more quality results based on the meaning of the search query. KEYWORDS: Semantic; Meta Search Engine; DBPedia; Information Retrieval; Clustering Download Full Text PDF |
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