Abstract
The knowledge database consists of facts about specific domain. Knowledge about specific domain is spread across in web. There is a need for some mechanism to represent the knowledge in a database for effective information retrieval through user query. In this paper we have implemented Knowledge Retrieval System (KRS) with user feedback to retrieve the most relevant answer. Knowledge is gathered for Ionic framework via web crawler from various sources and KRS is stored in a database after being analyzed, modeled and indexed. The indexed knowledge base is optimized by training it using a learning engine which incorporates user feedback. The ranking of the content, changes, based on user preference. The system is designed to be agnostic of the data that it works on and hence can be ported for various knowledge base domains. The system is capable to work on any type of knowledge information just by changing the retrieval data set. The result shows that user feedback improves the indexer mechanism and results in better retrieval.
Published Version
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