Spatiotemporal Extensions to a Controlled Natural Language

2012 ◽  
pp. 61-78 ◽  
Author(s):  
William R. Murray ◽  
Tomas Singliar
Keyword(s):  
Natural Language ◽  
Controlled Natural Language

scholarly journals Towards an Automated Model-Based Test Generation Approach from Controlled Natural Language Specifications Written in Arabic

2021 ◽  
Author(s):  
Moez Krichen ◽  
Seifeddine Mechti
Keyword(s):  
Natural Language ◽  
Test Generation ◽  
Arabic Language ◽  
Test Cases ◽  
New Model ◽  
Controlled Natural Language ◽  
Model Based ◽  
Case Grammar ◽  
Model Based Testing ◽  
Testing Approach

<div>We propose a new model-based testing approach which takes as input a set of requirements described in Arabic Controlled Natural Language (CNL) which is a subset of the Arabic language generated by a specific grammar. The semantics of the considered requirements is defined using the Case Grammar Theory (CTG). The requirements are translated into Transition Relations which serve as an input for test cases generation tools.</div>

scholarly journals Towards an Automated Model-Based Test Generation Approach from Controlled Natural Language Specifications Written in Arabic

2021 ◽  
Author(s):  
Moez Krichen ◽  
Seifeddine Mechti
Keyword(s):  
Natural Language ◽  
Test Generation ◽  
Arabic Language ◽  
Test Cases ◽  
New Model ◽  
Controlled Natural Language ◽  
Model Based ◽  
Case Grammar ◽  
Model Based Testing ◽  
Testing Approach

<div>We propose a new model-based testing approach which takes as input a set of requirements described in Arabic Controlled Natural Language (CNL) which is a subset of the Arabic language generated by a specific grammar. The semantics of the considered requirements is defined using the Case Grammar Theory (CTG). The requirements are translated into Transition Relations which serve as an input for test cases generation tools.</div>

Clinical Practice Ontology Automatic Learning from SOAP Reports

2016 ◽  
pp. 349-363
Author(s):  
David Mendes ◽  
Irene Pimenta Rodrigues ◽  
Carlos Fernandes Baeta
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Natural Language ◽  
Knowledge Base ◽  
Clinical Information ◽  
Knowledge Structure ◽  
Daily Routine ◽  
Automatic Learning ◽  
Controlled Natural Language ◽  
Practice Knowledge

We show how we implemented an end-to-end process to automatically develop a clinical practice knowledge base acquiring from SOAP notes. With our contribution we intend to overcome the “Knowledge Acquisition Bottleneck” problem by jump-starting the knowledge gathering from the most widely available source of clinical information that are natural language reports. We present the different phases of our process to populate automatically a proposed ontology with clinical assertions extracted from daily routine SOAP notes. The enriched ontology becomes a reasoning able knowledge base that depicts accurately and realistically the clinical practice represented by the source reports. With this knowledge structure in place and novel state-of-the-art reasoning capabilities, based in consequence driven reasoners, a clinical QA system based in controlled natural language is introduced that reveals breakthrough possibilities regarding the applicability of Artificial Intelligence techniques to the medical field.

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