ARAGOG Semantic Search Engine: Working, Implementation and Comparison with Keyword-Based Search Engines

Abstract

Seeing the exponential growth rate of knowledge and data all over the web, one can foresee that it would be difficult for data driven search engines to cope up with this fast pace of data generation effectively in future. Giving relevant, useful and close to accurate search results will be a big challenge given their current design and approach. Search Engines will be required to make a transit from keyword based search approach to semantic based search approach. It will be equally important for search engines to understand and derive the meaning of user query’s actual intent and provide results accordingly. In this backdrop we present our prototype – Aragog, which is even a step ahead than the conventional idea of a semantic search engine. This not only makes the user free from the hassle of browsing through hundreds of irrelevant results, but also generates results in an order that would match its intended context, with a high probability. The main motive behind innovation of Aragog is simple – Focus on user’s actual intent of search than number of results. The engine has been designed and tested and the results have been found to be exemplary. In comparison to traditional search engines, it produced more relevant results. Additionally, we have incorporated many other features such as synonym handling and explicit result display that make it all the more tempting to emerge as the next generation’s search engine format.