Optimized Prediction of Hard Keyword Queries Over Databases

Keyword Query Interface on databases gives easy access to data, but undergo from low ranking quality, i.e., low precision and/or recall. It would be constructive to recognize queries that are likely to have low ranking quality to improve the user satisfaction. For example, the system may suggest to the user alternative queries for such difficult queries. Goal of this paper is to predict the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, allowing for both the structure and the content of the database and the results of query. There are query difficulty prediction model but results indicate that even with structured data, finding the desired answers to keyword queries is still a hard task. Further, we will use linguistic features Such as morphological features, syntactical features, and semantic features for effective prediction of difficult keyword queries over database. Due to this, Time required for predicting the difficult keywords over large dataset is minimized and process becomes robust and accurate. DOI: 10.17762/ijritcc2321-8169.15078

Enhancing Query hardness detection with answer extraction using Ontology over the relational databases

Most of the information retrieval systems in the relational databases need the simpler keywords for the specific attribute in the database. But as the searching system becomes more and more popular the databases are also growing accordingly in gigantic manner. So it is obvious that users are always using complex queries to extract the much desired data from the database if they are not having any prior knowledge about the database structure. If the system is able to provide the complexity or difficulty of the query to the user then he/ she can rearrange the query in much simpler form to get the desired answer. So many systems are been existed to provide the answers for the difficult queries which are working in one or two aspects of the solving issues. So this paper represents an idea of extracting the hardness with the answer for the difficult query in more meaningful manner. The proposed idea is enriched with ontology for the semantic relations of the extracted features from the fired difficult query and inverted indices are used to catalyze the retrieval process more efficiently. DOI: 10.17762/ijritcc2321-8169.15081

1 Efficient Prediction of Difficult Keyword Queries over Databases

Abstract—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 this paper, we analyze 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. We evaluate our query difficulty prediction model against two effectiveness benchmarks for popular keyword search ranking methods. Our empirical results show that our model predicts the hard queries with high accuracy. Further, we present a suite of optimizations to minimize the incurred time overhead