出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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.
关键词：Search Engine; WWW; Web Content Mining; Inverted Indexing; Hidden Crawler; Distributed;
Web Crawler; Precision; Deep Web
