CONTEXT-AWARE MAP FROM ENTITIES TO CANONICAL FORMS

1 . A computer system, comprising:

one or more hardware processors; and

a memory device storing an instruction set executable by the one or more hardware processors that cause the computer system to perform operations comprising:

selecting at least one candidate job title from a portion of a job title taxonomy that corresponds with at least one job title in profile data of a target member account of a social network service;

for each respective candidate job title:

assembling, according to at least one encoded rule of a machine learning model for the respective candidate job title, feature vector data based in part on profile data of the target member account; and

calculating a probable job title score according to the machine learning model for the respective candidate job title;

identifying a select probable job title score from a plurality of probable job title scores;

creating an association between the at least one job title in the profile data of the target member account and a candidate job title that corresponds to the select probable job title score.

2 . The computer system as in claim 1 , further comprising:

building the portion of the job title taxonomy by creating a taxonomy relationship between a first candidate job title and a second candidate job title; and

wherein selecting at least one candidate job titles from the portion of the job title taxonomy comprises:

identifying at least one shared text segment between the first candidate job title and the at least one job title in the profile data of the target member account;

selecting the first candidate job title based on the at least one shared text segment; and

selecting the second candidate job title based on the taxonomy relationship.

3 . The computer system as in claim 2 , wherein building the portion of the job title taxonomy by creating a relationship between a first candidate job title and a second candidate job title comprises:

identify a first plurality of member accounts of the social network service with respective profile data that includes a first job title;

identify a first plurality of skills common to the respective profile data of the first plurality of member accounts;

identify a second plurality of member accounts of the social network service with respective profile data that includes a second job title;

identify a second plurality of skills common to the respective profile data of the second plurality of member accounts; and

creating a taxonomy relationship between the first job title and the second job title based on a threshold number of skills shared between the first and the second plurality of skills.

4 . The computer system as in claim 3 , wherein building the portion of the job title taxonomy occurs in an offline pre-processing mode; and

wherein creating the association between the at least one job title in the profile data of the target member account and a candidate job title that corresponds to the select probable job title score occurs in an online processing mode.

5 . The computer system as in claim 1 , wherein assembling, according to at least one encoded rule of a machine learning model for the respective candidate job title, feature vector data based in part on profile data of the target member account comprises:

accessing encoded data representative of a feature rule for a type of pre-defined feature;

accessing encoded data representative of at least one attribute of the profile data of the target member account that corresponds with the type of the pre-defined feature;

identifying a regression coefficient associated with the type of the pre-defined feature; and

assembling, according to the feature rule, a portion of the feature vector data for the target member account based on the at least one attribute of the profile data and the regression coefficient.

6 . The computer system as in claim 5 , wherein accessing encoded data representative of a feature rule for a type of pre-defined feature comprises:

accessing encoded data representative of a feature rule based on aggregate skills tags of a plurality of member accounts with profile data that includes the respective candidate job title.

7 . The computer system as in claim 5 , wherein accessing encoded data representative of a feature rule for a type of pre-defined feature comprises:

accessing encoded data representative of a feature rule based on aggregate industry designations of a plurality of member accounts with profile data that includes the respective candidate job title.

8 . The computer system as in claim 5 , wherein accessing encoded data representative of a feature rule for a type of pre-defined feature comprises:

accessing encoded data representative of a feature rule based on aggregate previous job titles of a plurality of member accounts with profile data that includes the respective candidate job title.

9 . A computer-implemented method, comprising:

selecting at least one candidate job title from a portion of a job title taxonomy that corresponds with at least one job title in profile data of a target member account of a social network service;

for each respective candidate job title:

assembling, according to at least one encoded rule of a machine learning model for the respective candidate job title, feature vector data based in part on profile data of the target member account; and

calculating, via at least on processor, a probable job title score according to the machine learning model for the respective candidate job title;

identifying a select probable job title score from a plurality of probable job title scores;

creating an association between the at least one job title in the profile data of the target member account and a candidate job title that corresponds to the select probable job title score.

10 . The computer-implemented method as in claim 9 , further comprising:

building the portion of the job title taxonomy by creating a taxonomy relationship between a first candidate job title and a second candidate job title; and

wherein selecting at least one candidate job titles from the portion of the job title taxonomy comprises:

identifying at least one shared text segment between the first candidate job title and the at least one job title in the profile data of the target member account;

selecting the first candidate job title based on the at least one shared text segment; and

selecting the second candidate job title based on the taxonomy relationship.

11 . The computer-implemented method as in claim 10 , wherein building the portion of the job title taxonomy by creating a relationship between a first candidate job title and a second candidate job title comprises:

identify a first plurality of member accounts of the social network service with respective profile data that includes a first job title;

identify a first plurality of skills common to the respective profile data of the first plurality of member accounts;

identify a second plurality of member accounts of the social network service with respective profile data that includes a second job title;

identify a second plurality of skills common to the respective profile data of the second plurality of member accounts; and

creating a taxonomy relationship between the first job title and the second job title based on a threshold number of skills shared between the first and the second plurality of skills.

12 . The computer-implemented method as in claim 11 , wherein building the portion of the job title taxonomy occurs in an offline pre-processing mode; and

wherein creating the association between the at least one job title in the profile data of the target member account and a candidate job title that corresponds to the select probable job title score occurs in an online processing mode.

13 . The computer-implemented method as in claim 9 , wherein assembling, according to at least one encoded rule of a machine learning model for the respective candidate job title, feature vector data based in part on profile data of the target member account comprises:

accessing encoded data representative of a feature rule for a type of pre-defined feature;

accessing encoded data representative of at least one attribute of the profile data of the target member account that corresponds with the type of the pre-defined feature,

identifying a regression coefficient associated with the type of the pre-defined feature; and

assembling, according to the feature rule, a portion of the feature vector data for the target member account based on the at least one attribute of the profile data and the regression coefficient.

14 . The computer-implemented method as in claim 13 , wherein accessing encoded data representative of a feature rule for a type of pre-defined feature comprises:

accessing encoded data representative of a feature rule based on aggregate skills tags of a plurality of member accounts with profile data that includes the respective candidate job title.

15 . The computer-implemented method as in claim 13 , wherein accessing encoded data representative of a feature rule for a type of pre-defined feature comprises:

accessing encoded data representative of a feature rule based on aggregate industry designations of a plurality of member accounts with profile data that includes the respective candidate job title.

16 . The computer-implemented method as in claim 13 , wherein accessing encoded data representative of a feature rule for a type of pre-defined feature comprises:

accessing encoded data representative of a feature rule based on aggregate previous job titles of a plurality of member accounts with profile data that includes the respective candidate job title.

17 . A non-transitory computer-readable medium storing executable instructions thereon, which, when executed by a processor, cause the processor to perform operations including:

selecting at least one candidate job title from a portion of a job title taxonomy that corresponds with at least one job title in profile data of a target member account of a social network service;

for each respective candidate job title:

assembling, according to at least one encoded rule of a machine learning model for the respective candidate job title, feature vector data based in part on profile data of the target member account; and

calculating a probable job title score according to the machine learning model for the respective candidate job title;

identifying a select probable job title score from a plurality of probable job title scores;

creating an association between the at least one job title in the profile data of the target member account and a candidate job title that corresponds to the select probable job title score.

18 . The non-transitory computer-readable medium as in claim 17 , further comprising:

building the portion of the job title taxonomy by creating a taxonomy relationship between a first candidate job title and a second candidate job title; and

wherein selecting at least one candidate job titles from the portion of the job title taxonomy comprises:

identifying at least one shared text segment between the first candidate job title and the at least one job title in the profile data of the target member account;

selecting the first candidate job title based on the at least one shared text segment; and

selecting the second candidate job title based on the taxonomy relationship.

19 . The non-transitory computer-readable medium as in claim 18 , wherein building the portion of the job title taxonomy by creating a relationship between a first candidate job title and a second candidate job title comprises:

identify a first plurality of member accounts of the social network service with respective profile data that includes a first job title;

identify a first plurality of skills common to the respective profile data of the first plurality of member accounts;

identify a second plurality of member accounts of the social network service with respective profile data that includes a second job title;

identify a second plurality of skills common to the respective profile data of the second plurality of member accounts; and

creating a taxonomy relationship between the first job title and the second job title based on a threshold number of skills shared between the first and the second plurality of skills.

20 . The non-transitory computer-readable medium as in claim 19 , wherein building the portion of the job title taxonomy occurs in an offline pre-processing mode; and

wherein creating the association between the at least one job title in the profile data of the target member account and a candidate job title that corresponds to the select probable job title score occurs in an online processing mode.