Enterprise knowledge graphs using user-based mining

Patent metadata

Field	Value
Publication number	US-2022019908-A1
Application number	US-202016933930-A
Country	US
Kind code	A1
Filing date	Jul 20, 2020
Priority date	Jul 20, 2020
Publication date	Jan 20, 2022
Grant date	—

Abstract

Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. A mining of a set of enterprise source documents within an enterprise intranet is performed, by a user-based mining system, to determine a plurality of entity names. An entity record is generated within a knowledge graph for a mined entity name from the linked entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name. The entity record includes attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name.

First claim

1 . A computer system comprising:
a memory storing computer-executable instructions; a processor configured to execute the instructions to:
perform, by a user-based mining system, a mining of a set of enterprise source documents within an enterprise intranet to determine a plurality of entity names that are trending and active in the enterprise intranet based on enterprise users and enterprise user activity;
generate an entity record within a knowledge graph for a mined entity name from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name, the entity record including attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name; and
display an entity page including at least a portion of the attributes of the entity record to a second user based on permissions of the second user to view the ones of the set of enterprise source documents associated with the mined entity name.

2 . The computer system of claim 1 , wherein the user-based mining system comprises a natural language based model.

3 . The computer system of claim 1 , wherein:
the entity record includes metadata defining supporting enterprise source documents for each of the attributes of the entity record; and the processor is configured to display respective ones of the portion of the attributes included in the entity page to the second user in response to determining that the second user has permission to access at least one of the enterprise source documents that supports the respective ones of the portion of the attributes.

4 . The computer system of claim 1 , wherein the enterprise user activity comprises at least one of meetings, emails, documents, acronyms, definitions, and related people properties.

5 . The computer system of claim 1 , wherein the enterprise user activity comprises one or more of how often a user discusses key phrases, whether the user is discussing the key phrases with known colleagues, documents authored by the user, and documents edited by the user.

6 . The computer system of claim 1 , wherein the processor is further configured to phase out stale topics based on an inactivity for a threshold period of time.

7 . The computer system of claim 1 , wherein the processor is configured to:
receive a curation action on the entity record from a first user associated with the entity record via the mining; and update the entity record based on the curation action.

8 . The computer system of claim 1 wherein the entity record is a project entity record and the entity schema defines an identifier, a name, one or more members, one or more related groups or sites, and one or more related documents.

9 . The computer system of claim 8 , wherein the entity schema further defines one or more managers, one or more related emails, or one or more related meetings.

10 . The computer system of claim 1 , wherein the processor is further configured to:
phase out stale topics based on an inactivity for a threshold period of time.

11 . A method of managing an entity record within a knowledge graph, comprising
performing, by a user-based mining system, a mining of a set of enterprise source documents within an enterprise intranet to determine a plurality of entity names that are trending and active in the enterprise intranet based on enterprise users and enterprise user activity; generating an entity record within a knowledge graph for a mined entity name from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name, the entity record including attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name; and displaying an entity page including at least a portion of the attributes of the entity record to a second user based on permissions of the second user to view the ones of the set of enterprise source documents associated with the mined entity name.

12 . The method of claim 11 , wherein the user-based mining system comprises a natural language based model.

13 . The method of claim 11 , wherein the enterprise user activity comprises at least one of meetings, emails, documents, acronyms, definitions, and related people properties.

14 . The method of claim 11 , wherein the enterprise user activity comprises how often a user discusses key phrases, whether the user is discussing the key phrases with known colleagues, documents authored by the user, and documents edited by the user.

15 . The method of claim 11 , further comprising executing a mechanism to invite users to edit information that is currently captured information.

16 . The method of claim 15 , wherein the invited users are selected based on a likelihood of being involved with a topic or having knowledge about the topic.

17 . A non-transitory computer-readable medium storing computer-executable instructions that when executed by a computer processor cause the computer processor to:
performing, by a user-based mining system, a mining of a set of enterprise source documents within an enterprise intranet to determine a plurality of entity names that are trending and active in the enterprise intranet based on enterprise users and enterprise user activity; generating an entity record within a knowledge graph for a mined entity name from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name, the entity record including attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name; and displaying an entity page including at least a portion of the attributes of the entity record to a second user based on permissions of the second user to view the ones of the set of enterprise source documents associated with the mined entity name.

18 . The non-transitory computer-readable medium of claim 17 , wherein the user-based mining system comprises a natural language based model.

19 . The non-transitory computer-readable medium of claim 17 , wherein the enterprise user activity comprises at least one of meetings, emails, documents, acronyms, definitions, and related people properties.

20 . The non-transitory computer-readable medium of claim 17 , wherein the enterprise user activity comprises how often a user discusses key phrases, and whether the user is discussing the key phrases with known colleagues, documents authored by the user, and documents edited by the user.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Meyerzon Dmitriy
• Khan Omar Zia
• Bikkula Ravi
• Aggarwal Khushboo
• Ahmed Hussein
• Tangirala Karthik

Classifications

• G06F40/279

  Recognition of textual entities · CPC title

• G06F16/9024 Primary

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

• G06F16/26

  Visual data mining; Browsing structured data · CPC title

• G06N5/025

  Extracting rules from data · CPC title

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title