Knowledge graph-based query in artificial intelligence chatbot with base query element detection and graph path generation

Patent metadata

Field	Value
Publication number	US-11328181-B2
Application number	US-201916550786-A
Country	US
Kind code	B2
Filing date	Aug 26, 2019
Priority date	Aug 26, 2019
Publication date	May 10, 2022
Grant date	May 10, 2022

Abstract

Generating a query result utilizing a knowledge graph in an artificial intelligence chatbot is provided. Characteristics of a query are identified. The characteristics of the query are mapped to base elements of the knowledge graph in the artificial intelligence chatbot. A set of query paths are generated in the knowledge graph based on the mapping of the characteristics of the query to the base elements of the knowledge graph. One or more query paths in the set of query paths in the knowledge graph are validated based on a respective score of each query path. A query result corresponding to the query is generated based on the validated one or more query paths in the knowledge graph.

First claim

What is claimed is:

1. A computer-implemented method for generating a query result utilizing a knowledge graph in an artificial intelligence chatbot, the computer-implemented method comprising:
identifying, by a computer, characteristics of a query;
mapping, by the computer, the characteristics of the query to base elements of the knowledge graph in the artificial intelligence chatbot;
generating, by the computer, a set of query paths in the knowledge graph based on the mapping of the characteristics of the query to the base elements of the knowledge graph;
validating, by the computer, one or more query paths in the set of query paths in the knowledge graph based on a respective score of each query path; and
generating, by the computer, a query result corresponding to the query based on the validated one or more query paths in the knowledge graph, wherein the base elements of the knowledge graph in the artificial intelligence chatbot are defined base query elements, and wherein the defined base query elements comprise an anchor element, a jump element, a filter element, and a target element, wherein the anchor element represents entities in the knowledge graph, the jump element represents relationships between data stored in the knowledge graph, the filter element represents filtering conditions on the data stored in the knowledge graph, and the target element represents target attributes of the data stored in the knowledge graph.

2. The computer-implemented method of claim 1 , further comprising:
receiving, by the computer, the query from a client device corresponding to a user via a network.

3. The computer-implemented method of claim 2 , further comprising:
transmitting, by the computer, the query result corresponding to the query to the client device corresponding to the user via the network.

4. The computer-implemented method of claim 1 , further comprising:
defining, by the computer, the base elements of the knowledge graph to match requirements of the query.

5. The computer-implemented method of claim 1 , further comprising:
calculating, by the computer, the respective score of each query path in the set of query paths in the knowledge graph based on multiple dimensions comprising order of words in the query, ground truth that validates the query result in the knowledge graph, and whether the query was submitted based on a query history.

6. The computer-implemented method of claim 1 , wherein the query is a natural language query.

7. The computer-implemented method of claim 1 , wherein the computer utilizes natural language processing to identify the characteristics of the query.

8. The computer-implemented method of claim 6 , wherein the defined base query elements correspond to a graph query language that can be identified by a graph database containing the knowledge graph, and further comprising:
converting the natural language query to the graph query language.

9. The computer-implemented method of claim 1 , wherein a valid query path in the validated one or more query paths has a corresponding query path score above a query path score threshold level.

10. The computer-implemented method of claim 1 , wherein a valid query path in the validated one or more query paths has a highest query path score.

11. The computer-implemented method of claim 1 , wherein the characteristics of the query include keywords contained in the query.

12. A computer system for generating a query result utilizing a knowledge graph in an artificial intelligence chatbot, the computer system comprising:
a bus system;
a storage device connected to the bus system, wherein the storage device stores program instructions; and
a processor connected to the bus system, wherein the processor executes the program instructions to:
identify characteristics of a query;
map the characteristics of the query to base elements of the knowledge graph in the artificial intelligence chatbot;
generate a set of query paths in the knowledge graph based on the mapping of the characteristics of the query to the base elements of the knowledge graph;
validate one or more query paths in the set of query paths in the knowledge graph based on a respective score of each query path; and
generate a query result corresponding to the query based on the validated one or more query paths in the knowledge graph, wherein the base elements of the knowledge graph in the artificial intelligence chatbot are defined base query elements, and wherein the defined base query elements comprise an anchor element, a jump element, a filter element, and a target element, wherein the anchor element represents entities in the knowledge graph, the jump element represents relationships between data stored in the knowledge graph, the filter element represents filtering conditions on the data stored in the knowledge graph, and the target element represents target attributes of the data stored in the knowledge graph.

13. The computer system of claim 12 , wherein the processor further executes the program instructions to:
receive the query from a client device corresponding to a user via a network.

14. The computer system of claim 13 , wherein the processor further executes the program instructions to:
transmit the query result corresponding to the query to the client device corresponding to the user via the network.

15. The computer system of claim 12 , wherein the processor further executes the program instructions to:
define the base elements of the knowledge graph to match requirements of the query.

16. The computer system of claim 12 , wherein the processor further executes the program instructions to:
calculate the respective score of each query path in the set of query paths in the knowledge graph based on multiple dimensions comprising order of words in the query, ground truth that validates the query result in the knowledge graph, and whether the query was submitted based on a query history.

17. A computer program product for generating a query result utilizing a knowledge graph in an artificial intelligence chatbot, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:
identifying, by the computer, characteristics of a query;
mapping, by the computer, the characteristics of the query to base elements of the knowledge graph in the artificial intelligence chatbot;
generating, by the computer, a set of query paths in the knowledge graph based on the mapping of the characteristics of the query to the base elements of the knowledge graph;
validating, by the computer, one or more query paths in the set of query paths in the knowledge graph based on a respective score of each query path; and
generating, by the computer, a query result corresponding to the query based on the validated one or more query paths in the knowledge graph, wherein the base elements of the knowledge graph in the artificial intelligence chatbot are defined base query elements, and wherein the defined base query elements comprise an anchor element, a jump element, a filter element, and a target element, wherein the anchor element represents entities in the knowledge graph, the jump element represents relationships between data stored in the knowledge graph, the filter element represents filtering conditions on the data stored in the knowledge graph, and the target element represents target attributes of the data stored in the knowledge graph.

Assignees

• IBM

Inventors

• Wang Wen
• Yin Jian Dong
• Cai Zhuo
• Fu Rong
• Sheng Hao
• Zhang Kang

Classifications

• G06F18/217

  Validation; Performance evaluation; Active pattern learning techniques · CPC title

• H04L51/02 Primary

  using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages · CPC title

• G06N5/02

  Knowledge representation; Symbolic representation · CPC title

• G06F16/9024 Primary

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

• G06F16/3322

  using system suggestions (G06F16/3325 takes precedence) · CPC title