Context Sensitive Search Engine

In this paper, we use context information extracted from the documents in the collection to improve the performance of the search engine. In first step, we extract context using Lucene, DBPedia-Spotlight, and Wordnet. As the second step, we build a graph using extracted context information. In the third step, in order to group similar contexts, we cluster context graph. In the fourth step, we re-score results using context-clusters and contextinformation of documents, as well as queries. In the fifth step, we implement a data collection tool to collect gold-standard data. In the sixth and final step, we compare the results of our algorithm with gold-standard data set. According to the experimental results, using context information may improve the search engine performance but the collection should be relatively big.