Shailja Dalmia, Ashwin T S and Ram Mohana Reddy Guddeti, National Institute of Technology Karnataka, India

With the ever-growing variety of information, the retrieval demands of different users are so multifarious that the traditional search engine cannot afford such heterogeneous retrieval results of huge magnitudes. Harnessing the advancements in a user-centered adaptive search engine will aid in groundbreaking retrieval results achieved efficiently for high-quality content. Previous work in this field have made using the excessive server load to achieve good retrieval results but with the limited extended ability and ignoring on demand generated content. To address this gap, we propose a novel model of adaptive search engine and describe how this model is realized in a distributed cluster environment. Using an improved current algorithm of topic-oriented web crawler with User Interface based Information Extraction Technique was able to produce a renewed set of user-centered retrieval results with higher efficiency than all existing methods. The proposed method was found to exceed by 1.5 times and two times for crawler and indexer, respectively than all prevailing methods with improved and highly precise results in extracting semantic information from Deep web.

Search Engine, WWW, Web Content Mining, Inverted Indexing, Hidden Crawler, Distributed Web Crawler, Precision, Deep Web