Semantic search engine and its strategies with IAN encoder

Abstract

A semantic based approach is better in many ways than the keyword based searches. As the wide amount of information on the internet is exceling daily, the keyword based approach in many commercial search engines is failing to retrieve correct information without getting the knowledge and need of user query. Since the keyword based search could search for the particular language having that keyword, thus many commercial search engine is one language oriented. We present our solution for performing cross lingual semantic based search. First, we introduce the UNL, the approach followed and the frameworks IAN and EUGENE which are used as enconverter and deconverter for the system. Second, we focus on the failure of some other meaning based search engines in brief. Third, we present our semantic model semiautomated cross lingual semantic model, which encapsulate and sharpen previously proposed models. Forth, we report our analysis of search success using these data, which confirm and extend previous findings. Finally, we demonstrate our model on different strategies of search engine. Together, our semantic search engine is another approach that not only distinguishes between the search strategies but is able to perform meaning based, cross-lingual search using IAN encoder having dictionaries and rules of different languages.