scholarly journals A Novel IR for Relational Database using Optimize Query Building

2019 ◽  
pp. 251-257
Author(s):  
Sangeeta Vishwakarma ◽  
Avinash Dhole
Keyword(s):  
Natural Language ◽  
Search Engine ◽  
Query Optimization ◽  
Relational Database ◽  
Execution Time ◽  
Optimization Approach ◽  
Complex Query ◽  
Query Intent ◽  
Pos Tagger ◽  
Sql Query

The different type search engine like Google, binge, AltaVista is used to fetch the information from the database by easy language. The non-technical employee they don’t understand the database and query cannot access the database. The proposed system is performing work as a search engine where users can fetch the information from the database by natural human sounding language. The previous existing system doesn’t able to solve queries in one easy statement. The structured query approach, while expressive and powerful, is not easy for naive users. The keyword-based approach is very friendly to use, but cannot express complex query intent accurately. This paper emphasis on Natural Language based query processor. We have proposed the use of query optimization approach to convert complex NLP query to SQL query, SPAM word removal, POS tagger applied over NL query and concluded that execution time lesser when query size increases.

Natural-Language-based Generation of Search Results in a Meaning-based Multilingual Search Engine

2006 ◽  
Vol 23 (5) ◽  
pp. 313-319
Author(s):  
Yogesh P Awate ◽  
Jagger Bodas ◽  
Sachin Deshpande ◽  
Pushpak Bhattacharyya
Keyword(s):  
Natural Language ◽  
Search Engine ◽  
Search Results

Dynamic Query Optimization Approach for Semantic Database Grid

2006 ◽  
Vol 21 (4) ◽  
pp. 597-608 ◽  
Author(s):  
Xiao-Qing Zheng ◽  
Hua-Jun Chen ◽  
Zhao-Hui Wu ◽  
Yu-Xin Mao
Keyword(s):  
Query Optimization ◽  
Optimization Approach ◽  
Semantic Database ◽  
Dynamic Query

MPP SQL Query Optimization with RTCG

2018 ◽  
pp. 228-249
Author(s):  
K. T. Sridhar ◽  
M. A. Sakkeer ◽  
Shiju Andrews ◽  
Jimson Johnson
Keyword(s):  
Query Optimization ◽  
Sql Query

scholarly journals Empowering Natural Language Interfaces to Databases with Aggregations

2021 ◽  
Vol 12 (5) ◽  
Author(s):  
Alexandre F. Novello ◽  
Marco A. Casanova
Keyword(s):  
Natural Language ◽  
Relational Database ◽  
Natural Language Interfaces ◽  
Aggregation Functions ◽  
Natural Language Interface ◽  
Measurement Units ◽  
Database Interface ◽  
Multiple Attributes ◽  
Generic Module ◽  
Query Aggregation

A Natural Language Interface to Database (NLIDB) refers to a database interface that translates a question asked in natural language into a structured query. Aggregation questions express aggregation functions, such as count, sum, average, minimum and maximum, and optionally a group by clause and a having clause. NLIDBs deliver good results for standard questions but usually do not deal with aggregation questions. The main contribution of this article is a generic module, called GLAMORISE (GeneraL Aggregation MOdule using a RelatIonal databaSE), that extends NLIDBs to cope with aggregation questions. GLAMORISE covers aggregations with ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic recognition of measurement units, and aggregations in attributes with compound names.

VuFind

2013 ◽  
pp. 73-99 ◽  
Author(s):  
Demian Katz ◽  
Andrew Nagy
Keyword(s):  
Open Source ◽  
Search Engine ◽  
Relational Database ◽  
Data Retrieval ◽  
Similarity Analysis ◽  
The Core ◽  
Front End ◽  
Informative Case ◽  
Key Features

Apache Solr, an open source Java-based search engine, forms the core of many Library 2.0 products. The use of an index in place of a relational database allows faster data retrieval along with key features like faceting and similarity analysis that are not practical in the previous generation of library software. The popular VuFind discovery tool was built to provide a library-friendly front-end for Solr’s powerful searching capabilities, and its development provides an informative case study on the use of Solr in a library setting. VuFind is just one of many library packages using Solr, and examples like Blacklight, Summon, and the eXtensible Catalog project show other possible approaches to its use.

Two New Challenging Resources to Evaluate Natural Language Interfaces to Databases Generated Based on Geobase and Geoquery

2021 ◽  
pp. 70-100
Author(s):  
Juan Javier González-Barbosa ◽  
Juan Frausto Solís ◽  
Juan Paulo Sánchez-Hernández ◽  
Julia Patricia Sanchez-Solís
Keyword(s):  
Natural Language ◽  
Relational Database ◽  
Normalization Procedure ◽  
Natural Language Interfaces ◽  
Language Problems

Databases and corpora are essential resources to evaluate the performance of Natural Language Interfaces to Databases (NLIDB). The Geobase database and the Geoquery corpus (Geoquery250 and Geoquery880) are among the most commonly used. In this chapter, the authors analyze both resources to offer two elaborate resources: 1) N-Geobase, which is a relational database, and 2) the corpus Geoquery270. The former follows the standard normalization procedure, then N-Geobase has a schema similar to enterprise databases. Geoquery270 consists of 270 queries selected from Geoquery880, preserving the same kind of natural language problems as Geoquery880, but with more challenging issues for an NLIDB than Geoquery250. To evaluate the new resources, they compared the performance of the NLIDB using Geoquery270 and Geoquery250. The results indicated that Geoquery270 was the harder corpus, while Geoquery250 is the easier one. Consequently, this chapter offers a broader range of resources to NLIDB designers.

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