Improved Structured Robustness (I-SR): A Novel Approach to Predict Hard Keyword Queries

摘要

Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. It would be useful to identify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In the existing work, analyzes the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, considering both the structure and the content of the database and the query results. However, in this system numbers of issues are there to address. One of the main issues present in the existing work is that, at the time keyword prediction only user submitted keyword will be used for the prediction of the results. The existing work won't concentrate about the semantic meaning present among the key words that are submitted by the users, which will lead to inaccurate result retrieval. To overcome this problem in the proposed work, the semantic based key word prediction is proposed by using ontology-based representation in which the semantic meaning of the keywords will be analyzed by using the Word Net tool. This will lead to an accurate to k retrieval of document due to consideration of the semantic meaning of the documents in search engine.