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

Author(s):  
Anup Pillai ◽  
Vandana Mukherjee ◽  
Chaitanya Shivade ◽  
Amin Katouzian ◽  
Marina Bendersky ◽  
...  
Author(s):  
Hongxia Liu ◽  
Qingcheng Hu ◽  
Yong Zhang ◽  
Chunxiao Xing ◽  
Ming Sheng

2021 ◽  
pp. 434-439
Author(s):  
Yinnian Lin ◽  
Minhao Zhang ◽  
Ruoyu Zhang ◽  
Lei Zou

1993 ◽  
Vol 4 (4) ◽  
pp. 21-30 ◽  
Author(s):  
F. A. Mohammed ◽  
Khaled Nasser ◽  
H. M. Harb

2008 ◽  
Vol 21 (8) ◽  
pp. 946-950 ◽  
Author(s):  
Ali Ghobadi Tapeh ◽  
Maseud Rahgozar

2018 ◽  
Vol 46 (4) ◽  
pp. 214-224 ◽  
Author(s):  
Moritz Schubotz ◽  
Philipp Scharpf ◽  
Kaushal Dudhat ◽  
Yash Nagar ◽  
Felix Hamborg ◽  
...  

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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