MLM-Based Automated Query Generation for CDSS Evidence Support

2014 ◽  
pp. 296-299 ◽  
Author(s):  
Muhammad Afzal ◽  
Maqbool Hussain ◽  
Taqdir Ali ◽  
Wajahat Ali Khan ◽  
Sungyoung Lee ◽  
...  
Keyword(s):  
Query Generation

scholarly journals SPARQL Query Generator (SQG)

2021 ◽  
Author(s):  
Yanji Chen ◽  
Mieczyslaw M. Kokar ◽  
Jakub J. Moskal
Keyword(s):  
Data Retrieval ◽  
Data Representation ◽  
Sparql Query ◽  
Experimental Results ◽  
Generation Process ◽  
Manual Work ◽  
Retrieval Systems ◽  
Large Numbers ◽  
Query Generation ◽  
Good Coverage

AbstractThis paper describes a program—SPARQL Query Generator (SQG)—which takes as input an OWL ontology, a set of object descriptions in terms of this ontology and an OWL class as the context, and generates relatively large numbers of queries about various types of descriptions of objects expressed in RDF/OWL. The intent is to use SQG in evaluating data representation and retrieval systems from the perspective of OWL semantics coverage. While there are many benchmarks for assessing the efficiency of data retrieval systems, none of the existing solutions for SPARQL query generation focus on the coverage of the OWL semantics. Some are not scalable since manual work is needed for the generation process; some do not consider (or totally ignore) the OWL semantics in the ontology/instance data or rely on large numbers of real queries/datasets that are not readily available in our domain of interest. Our experimental results show that SQG performs reasonably well with generating large numbers of queries and guarantees a good coverage of OWL axioms included in the generated queries.

scholarly journals DialSQL: Dialogue Based Structured Query Generation

2018 ◽  
Author(s):  
Izzeddin Gur ◽  
Semih Yavuz ◽  
Yu Su ◽  
Xifeng Yan
Keyword(s):  
Query Generation

scholarly journals RECPARSER: A Recursive Semantic Parsing Framework for Text-to-SQL Task

2020 ◽  
Author(s):  
Yu Zeng ◽  
Yan Gao ◽  
Jiaqi Guo ◽  
Bei Chen ◽  
Qian Liu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Search Space ◽  
Layer By Layer ◽  
Semantic Parsing ◽  
Query Generation ◽  
Sql Query ◽  
Generation Problem

Neural semantic parsers usually fail to parse long and complicated utterances into nested SQL queries, due to the large search space. In this paper, we propose a novel recursive semantic parsing framework called RECPARSER to generate the nested SQL query layer-by-layer. It decomposes the complicated nested SQL query generation problem into several progressive non-nested SQL query generation problems. Furthermore, we propose a novel Question Decomposer module to explicitly encourage RECPARSER to focus on different components of an utterance when predicting SQL queries of different layers. Experiments on the Spider dataset show that our approach is more effective compared to the previous works at predicting the nested SQL queries. In addition, we achieve an overall accuracy that is comparable with state-of-the-art approaches.

Query Generation for Web Search Based on Spatio-Temporal Features of TV Program

2019 ◽  
pp. 82-92
Author(s):  
Honoka Kakimoto ◽  
Yuanyuan Wang ◽  
Yukiko Kawai ◽  
Kazutoshi Sumiya
Keyword(s):  
Web Search ◽  
Temporal Features ◽  
Tv Program ◽  
Query Generation ◽  
Spatio Temporal

Adaptive linear list reorganization under a generalized query system

1995 ◽  
Vol 32 (03) ◽  
pp. 793-804 ◽  
Author(s):  
R. S. Valiveti ◽  
B. J. Oommen ◽  
J. R. Zgierski
Keyword(s):  
Simple Model ◽  
Query Processing ◽  
Generation Mechanism ◽  
Linear List ◽  
Query Generation ◽  
Query System ◽  
The Individual

We consider the problem of reorganizing a linear list, when the individual queries consist of accesses to a subset of the elements stored, as opposed to the individual elements themselves. In this paper, which to our knowledge represents the first reported result in this model of query processing, we first propose a simple model for a query generator which emits set queries. Subsequently, we present extensions to the classical move-to-front (MTF) and transposition (TR) rules under this generalized query generation mechanism and analyze their performance.

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