US 11,983,640 B2
Generating question templates in a knowledge-graph based question and answer system
Zi Ming Huang, Beijing (CN); Jian Wang, Beijing (CN); Jing Li, Beijing (CN); Jian Min Jiang, Beijing (CN); Ke Wang, Beijing (CN); and Xin Ni, Beijing (CN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Dec. 30, 2019, as Appl. No. 16/730,083.
Prior Publication US 2021/0201174 A1, Jul. 1, 2021
Int. Cl. G06F 16/2458 (2019.01); G06F 16/332 (2019.01); G06F 16/901 (2019.01); G06F 40/186 (2020.01); G06F 40/205 (2020.01); G06N 5/02 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06N 5/04 (2013.01) [G06F 16/2458 (2019.01); G06F 16/3329 (2019.01); G06F 16/9024 (2019.01); G06F 40/186 (2020.01); G06F 40/205 (2020.01); G06N 5/02 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
parsing a graph database query relating to an automated artificial intelligence question and answer (QA) system using a predefined schema, using a parser engine, the parsing comprising:
processing a where clause in the graph database query to generate a where clause processor output, comprising:
extracting a first plurality of values, comprising a node, an operation, a function, and a condition, from the graph database query;
processing a return clause in the graph database query to generate a return clause processor output, comprising:
extracting a second plurality of values, comprising a return context and a return function, from the graph database query; and
identifying a QA template rule relating to the graph database query, based on a match clause in the graph database query, comprising:
selecting the QA template rule from a collection of QA template rules based on the match clause; and
generating a natural language question template based on the where clause processor output, the return clause processor output, and the identified QA template rule, using a template generator; and
providing the natural language question template to the automated QA system so that the QA system can generate a response to a natural language question using the natural language question template.