scholarly journals Answering Complex Queries on Legal Networks: A Direct and a Structured IR Approaches

2018 ◽  
pp. 451-464 ◽  
Author(s):  
Nada Mimouni ◽  
Adeline Nazarenko ◽  
Sylvie Salotti
Keyword(s):  
Complex Queries

scholarly journals GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

2007 ◽  
Vol 45 (4) ◽  
pp. 839-852 ◽  
Author(s):  
Chi-Ren Shyu ◽  
Matt Klaric ◽  
Grant J. Scott ◽  
Adrian S. Barb ◽  
Curt H. Davis ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Geospatial Information ◽  
Complex Queries ◽  
Indexing System ◽  
Content Mining

scholarly journals Composing Questions through Conceptual Authoring

2007 ◽  
Vol 33 (1) ◽  
pp. 105-133 ◽  
Author(s):  
Catalina Hallett ◽  
Donia Scott ◽  
Richard Power
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Free Text ◽  
Risk Averse ◽  
Proof Of Concept ◽  
Concept System ◽  
Complex Queries ◽  
Extensive Training ◽  
Question Answering Systems ◽  
Medical Histories

This article describes a method for composing fluent and complex natural language questions, while avoiding the standard pitfalls of free text queries. The method, based on Conceptual Authoring, is targeted at question-answering systems where reliability and transparency are critical, and where users cannot be expected to undergo extensive training in question composition. This scenario is found in most corporate domains, especially in applications that are risk-averse. We present a proof-of-concept system we have developed: a question-answering interface to a large repository of medical histories in the area of cancer. We show that the method allows users to successfully and reliably compose complex queries with minimal training.

scholarly journals Design principles of the Metadata Querying Language (MQL) implemented in the ATLAS Metadata Interface (AMI) ecosystem

2020 ◽  
Vol 245 ◽  
pp. 04044
Author(s):  
Jérôme Fulachier ◽  
Jérôme Odier ◽  
Fabian Lambert
Keyword(s):  
Query Language ◽  
Design Principles ◽  
Domain Specific Language ◽  
Specific Language ◽  
Domain Specific ◽  
Complex Queries ◽  
Structured Query Language

This document describes the design principles of the Metadata Querying Language (MQL) implemented in ATLAS Metadata Interface (AMI), a metadata-oriented domain-specific language allowing to query databases without knowing the relation between tables. With this simplified yet generic grammar, MQL permits writing complex queries more simply than with Structured Query Language (SQL).

Fauce

2021 ◽  
Vol 14 (11) ◽  
pp. 1950-1963
Author(s):  
Jie Liu ◽  
Wenqian Dong ◽  
Qingqing Zhou ◽  
Dong Li
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
The State ◽  
Light Weight ◽  
Critical Problem ◽  
Estimation Errors ◽  
Cardinality Estimation ◽  
Complex Queries ◽  
Uncertainty Information ◽  
Deep Learning Model

Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results. However, these estimators struggle to provide accurate results for complex queries, due to not capturing real inter-column and inter-table correlations. Furthermore, none of these estimators contain the uncertainty information about their estimations. In this paper, we present a join cardinality estimator called Fauce. Fauce learns the correlations across all columns and all tables in the database. It also contains the uncertainty information of each estimation. Among all studied learned estimators, our results are promising: (1) Fauce is a light-weight estimator, it has 10× faster inference speed than the state of the art estimator; (2) Fauce is robust to the complex queries, it provides 1.3×--6.7× smaller estimation errors for complex queries compared with the state of the art estimator; (3) To the best of our knowledge, Fauce is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model.

scholarly journals A Domain Independent Natural Language Interface to Databases Capable of Processing Complex Queries

2005 ◽  
pp. 833-842 ◽  
Author(s):  
Rodolfo A. Pazos Rangel ◽  
O. Joaquín Pérez ◽  
B. Juan Javier González ◽  
Alexander Gelbukh ◽  
Grigori Sidorov ◽  
...  
Keyword(s):  
Natural Language ◽  
Complex Queries ◽  
Natural Language Interface ◽  
Domain Independent

Wavelets for Querying Multidimensional Datasets

2011 ◽  
pp. 1196-1200
Author(s):  
Cyrus Shahabi ◽  
Dimitris Sacharidis ◽  
Mehrdad Jahangiri
Keyword(s):  
Statistical Analysis ◽  
Wavelet Transformation ◽  
Storage Capacity ◽  
Ongoing Research ◽  
Complex Queries ◽  
Multidimensional Datasets ◽  
Processing Power ◽  
Continuous Interaction ◽  
And Storage ◽  
Aggregate Functions

Following the constant technological advancements that provide more processing power and storage capacity, scientific applications have emerged as a new field of interest for the database community. Such applications, termed Online Science Applications (OSA), require continuous interaction with datasets of multidimensional nature, mainly for performing statistical analysis. OSA can seriously benefit from the ongoing research for OLAP systems and the pre-calculation of aggregate functions for multidimensional datasets. One of the tools that we see fit for the task in hand is the wavelet transformation. Due to its inherent multi-resolution properties, wavelets can be utilized to provide progressively approximate and eventually fast exact answers to complex queries in the context of Online Science Applications.

Information Extraction for Call for Paper

2015 ◽  
Vol 6 (4) ◽  
pp. 35-49 ◽  
Author(s):  
Laurent Issertial ◽  
Hiroshi Tsuji
Keyword(s):  
Text Mining ◽  
Information Extraction ◽  
False Positive ◽  
Relevant Information ◽  
End User ◽  
Rule Based ◽  
Complex Queries ◽  
Mining Algorithm ◽  
Text Searching ◽  
Searching Method

This paper proposes a system called CFP Manager specialized on IT field and designed to ease the process of searching conference suitable to one's need. At present, the handling of CFP faces two problems: for emails, the huge quantity of CFP received can be easily skimmed through. For websites, the reviewing of some of the main CFP aggregators available online points out the lack of usable criteria. This system proposes to answer to these problems via its architecture consisting of three components: firstly an Information Extraction module extracting relevant information (as date, location, etc...) from CFP using rule based text mining algorithm. The second component enriches the now extracted data with external one from ontology models. Finally the last one displays the said data and allows the end user to perform complex queries on the CFP dataset and thus allow him to only access to CFP suitable for him. In order to validate the authors' proposal, they eventually process the well-known precision / recall metric on our information extraction component with an average of 0.95 for precision and 0.91 for recall on three different 100 CFP dataset. This paper finally discusses the validity of our approach by confronting our system for different queries with two systems already available online (WikiCFP and IEEE Conference Search) and basic text searching approach standing for searching in an email box. On a 100 CFP dataset with the wide variety of usable data and the possibility to perform complex queries we surpass basic text searching method and WikiCFP by not returning the false positive usually returned by them and find a result close to the IEEE system.

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