Deep-gAnswer: A Knowledge Based Question Answering System

2021 ◽  
pp. 434-439
Author(s):  
Yinnian Lin ◽  
Minhao Zhang ◽  
Ruoyu Zhang ◽  
Lei Zou
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Knowledge Based

A Knowledge-Based Health Question Answering System

2017 ◽  
pp. 286-291 ◽  
Author(s):  
Hongxia Liu ◽  
Qingcheng Hu ◽  
Yong Zhang ◽  
Chunxiao Xing ◽  
Ming Sheng
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Knowledge Based ◽  
Health Question

A knowledge-based question answering system to provide cognitive assistance to radiologists

2019 ◽  
Author(s):  
Anup Pillai ◽  
Vandana Mukherjee ◽  
Chaitanya Shivade ◽  
Amin Katouzian ◽  
Marina Bendersky ◽  
...  
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Knowledge Based ◽  
Cognitive Assistance

A knowledge based Arabic question answering system (AQAS)

1993 ◽  
Vol 4 (4) ◽  
pp. 21-30 ◽  
Author(s):  
F. A. Mohammed ◽  
Khaled Nasser ◽  
H. M. Harb
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Knowledge Based

A knowledge-based question answering system for B2C eCommerce

2008 ◽  
Vol 21 (8) ◽  
pp. 946-950 ◽  
Author(s):  
Ali Ghobadi Tapeh ◽  
Maseud Rahgozar
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Knowledge Based

scholarly journals Introducing MathQA: a Math-Aware question answering system

2018 ◽  
Vol 46 (4) ◽  
pp. 214-224 ◽  
Author(s):  
Moritz Schubotz ◽  
Philipp Scharpf ◽  
Kaushal Dudhat ◽  
Yash Nagar ◽  
Felix Hamborg ◽  
...  
Keyword(s):  
Mathematical Knowledge ◽  
Design Methodology ◽  
Question Answering ◽  
User Study ◽  
Content Type ◽  
Question Answering System ◽  
Knowledge Based ◽  
Natural Language Question ◽  
Language Question

Purpose This paper aims to present an open source math-aware Question Answering System based on Ask Platypus. Design/methodology/approach The system returns as a single mathematical formula for a natural language question in English or Hindi. These formulae originate from the knowledge-based Wikidata. The authors translate these formulae to computable data by integrating the calculation engine sympy into the system. This way, users can enter numeric values for the variables occurring in the formula. Moreover, the system loads numeric values for constants occurring in the formula from Wikidata. Findings In a user study, this system outperformed a commercial computational mathematical knowledge engine by 13 per cent. However, the performance of this system heavily depends on the size and quality of the formula data available in Wikidata. As only a few items in Wikidata contained formulae when the project started, the authors facilitated the import process by suggesting formula edits to Wikidata editors. With the simple heuristic that the first formula is significant for the paper, 80 per cent of the suggestions were correct. Originality/value This research was presented at the JCDL17 KDD workshop.

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