QA 3 : A natural language approach to question answering over RDF data cubes

Semantic Web ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 587-604 ◽  
Author(s):  
Maurizio Atzori ◽  
Giuseppe M. Mazzeo ◽  
Carlo Zaniolo
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Data Cubes ◽  
Rdf Data

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.

A survey of question answering in natural language processing

Poetics ◽  
1990 ◽  
Vol 19 (1-2) ◽  
pp. 99-120
Author(s):  
Stefan Wermter ◽  
Wendy G. Lehnert
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering

Performance of natural language classifiers in a question-answering system

2017 ◽  
Vol 61 (4/5) ◽  
pp. 14:1-14:10 ◽  
Author(s):  
R. Bakis ◽  
D. P. Connors ◽  
P. Dube ◽  
P. Kapanipathi ◽  
A. Kumar ◽  
...  
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Question Answering System
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