Query data structure representation

Patent metadata

Field	Value
Publication number	US-2017371925-A1
Application number	US-201615191220-A
Country	US
Kind code	A1
Filing date	Jun 23, 2016
Priority date	Jun 23, 2016
Publication date	Dec 28, 2017
Grant date	—

Abstract

A system and method for generating a data structure for an input query are provided. In example embodiments, the system receives an input query comprising of a plurality of terms. A data structure is generated comprising of a root node and lower level nodes, the root node indicating choices available for the query input, the lower level nodes including a first node with a first term of the input query and a second node with a second term of the input query. The first node is mapped to a first category with a first confidence score indicating a confidence of the mapping of the first node to the first category. The second node is mapped to a second category with a second confidence score indicating a confidence of the mapping of the second node to the second category. The input query is rewritten based on the generated data structure

First claim

What is claimed is:

1 . A system comprising:
a processor, and a memory including instructions, which when executed by the processor, cause the processor to: receive an input query comprising of a plurality of terms; generate a data structure comprising:
a root node indicating choices available for the query input; and
lower level nodes including a first node with a first term of the input query and a second node with a second term of the input query;
a mapping of the first node to a first category with a first confidence score indicating a confidence of the mapping of the first node to the first category; and
a mapping of the second node to a second category with a second confidence score indicating a confidence of the mapping of the second node to the second category; and
rewrite the input query based on the generated data structure.

2 . The system of claim 1 , wherein:
the first category and second category are nodes of a second data structure.

3 . The system of claim 1 , wherein:
the rewritten input query is in a format compatible to a search engine.

4 . The system of claim 1 , further comprising:
caching the generated data structure for the input query; receiving a second input query; determining the terms of the input query is included in the second input query based on a comparison of the input query and the second input query, the second input query including additional terms not present in the input query; updating the cached data structure to correspond to the second input query, the updating including adding additional lower level nodes that include the additional terms being mapped to corresponding categories along with corresponding confidence scores.

5 . The system of claim 1 , wherein:
the first category is a first interpretation of a term; and the second category is a second interpretation of the term.

6 . The system of claim 1 , wherein:
the first confidence score is calculated based on member activity data indicating a percentage of member activity associating the first term to the first category; and the second confidence score is calculated based on member activity data indicating a percentage of member activity associating the second term to the second category.

7 . The system of claim 1 , wherein:
the first confidence score is calculated based on member profile data indicating a percentage of member profile data associating the first term to the first category; and the second confidence score is calculated based on member profile data indicating a percentage of member profile data associating the second term to the second category.

8 . The system of claim 1 , further comprising:
retrieving search results using the rewritten query.

9 . A method comprising:
using one or more computer processors:
receiving an input query comprising of a plurality of terms from a user;
generating a data structure comprising:
a root node indicating choices available for the query input;
lower level nodes including a first node with a first term of the input query and a second node with a second term of the input query;
a mapping of the first node to a first category with a first confidence score indicating a confidence of the mapping of the first node to the first category; and
a mapping of the second node to a second category with a second confidence score indicating a confidence of the mapping of the second node to the second category; and

rewriting the input query based on the generated data structure.

10 . The method of claim 9 , wherein:
the first category and second category are nodes of a second data structure.

11 . The method of claim 9 , wherein:
the rewritten input query is in a format compatible to a search engine.

12 . The method of claim 9 , further comprising:
caching the generated data structure for the input query; receiving a second input query; determining the terms of the input query is included in the second input query based on a comparison of the input query and the second input query, the second input query including additional terms not present in the input query; updating the cached data structure to correspond to the second input query, the updating including adding additional lower level nodes that include the additional terms being mapped to corresponding categories along with corresponding confidence scores.

13 . The method of claim 9 , further comprising:
the first category is a first interpretation of a term; and the second category is a second interpretation of the term.

14 . The method of claim 9 , wherein:
the first confidence score is calculated based on member activity data indicating a percentage of member activity associating the first term to the first category; and the second confidence score is calculated based on member activity data indicating a percentage of member activity associating the second term to the second category.

15 . The method of claim 9 , further comprising:
the first confidence score is calculated based on member profile data indicating a percentage of member profile data associating the first term to the first category; and the second confidence score is calculated based on member profile data indicating a percentage of member profile data associating the second term to the second category.

16 . The method of claim 9 , further comprising:
retrieving search results using the rewritten query.

17 . A machine-readable medium not having any transitory signals and storing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:
receiving an input query comprising of a plurality of terms from a user; generating a data structure comprising:
a root node indicating choices available for the query input; and
lower level nodes including a first node with a first term of the input query and a second node with a second term of the input query;
a mapping of the first node to a first category with a first confidence score indicating a confidence of the mapping of the first node to the first category; and
a mapping of the second node to a second category with a second confidence score indicating a confidence of the mapping of the second node to the second category; and
rewriting the input query based on the generated data structure.

18 . The machine-readable medium of claim 17 , wherein the operations further comprise:
caching the generated data structure for the input query; receiving a second input query; determining the terms of the input query is included in the second input query based on a comparison of the input query and the second input query, the second input query including additional terms not present in the input query; updating the cached data structure to correspond to the second input query, the updating including adding additional lower level nodes that include the additional terms being mapped to corresponding categories along with corresponding confidence scores.

19 . The machine-readable medium of claim 17 , wherein:
the first category is a first interpretation of a term; and the second category is a second interpretation of the term.

20 . The machine-readable medium of claim 17 , wherein:
the first confidence score is calculated based on member activity data indicating a percentage of member activity associating the first term to the first category; and the second confidence score is calculated based on member activity data indicating a percen

Assignees

• Linkedin Corp

Inventors

• Arya Dhruv
• Punjani Diwakar
• Lad Abhimanyu

Classifications

• G06F17/30448 Primary

  Physics · mapped topic

• G06F17/30289

  Physics · mapped topic

• G06F17/30598

  Physics · mapped topic

• G06F17/3053

  Physics · mapped topic

• G06F17/30457

  Physics · mapped topic