US 11,709,878 B2
Enterprise knowledge graph
Dmitriy Meyerzon, Bellevue, WA (US); Jeffrey Wight, Kirkland, WA (US); Andrei Razvan Popov, Woodinville, WA (US); Andrei-Alin Corodescu, Oslo (NO); Omar Faruk, Oslo (NO); Jan-Ove Karlberg, Troms (NO); Åge Andre Kvalnes, Fetsund (NO); Helge Grenager Solheim, Oslo (NO); Thuy Duong, Seattle, WA (US); Simon Thoresen Hult, Oslo (NO); Ivan Korostelev, London (GB); Matteo Venanzi, London (GB); John Guiver, Saffron Walden (GB); John Michael Winn, Cambridge (GB); Vladimir V. Gvozdev, Sammamish, WA (US); Nikita Voronkov, Bothell, WA (US); Chia-Jiun Tan, Bellevue, WA (US); and Alexander Armin Spengler, Cambridge (GB)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Oct. 14, 2019, as Appl. No. 16/601,050.
Prior Publication US 2021/0110278 A1, Apr. 15, 2021
Int. Cl. G06F 16/00 (2019.01); G06F 16/35 (2019.01); G06N 5/02 (2023.01); G06F 16/93 (2019.01); G06F 16/901 (2019.01); G06F 16/28 (2019.01); G06F 18/23 (2023.01)
CPC G06F 16/355 (2019.01) [G06F 16/288 (2019.01); G06F 16/9024 (2019.01); G06F 16/93 (2019.01); G06F 18/23 (2023.01); G06N 5/02 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer system comprising:
a memory storing computer-executable instructions; and
a processor configured to execute the instructions to:
perform a mining of a set of enterprise source documents within an enterprise intranet to determine a plurality of entity names, wherein the processor is configured to perform the mining of the set of enterprise source documents by:
comparing the set of enterprise source documents to a set of templates defining potential entity attributes to identify instances within the set of enterprise source documents;
partitioning the instances by potential entity names into a plurality of partitions; and
clustering the instances within each partition to identify the mined entity name for each partition using an unsupervised machine learning process that iteratively finds groupings among extracts of the enterprise source documents including the instances until a stable probability distribution is reached;
generate an entity record within a knowledge graph for a mined entity name from the plurality of 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;
receive a curation action on the entity record from a first user associated with the entity record via the mining;
update the entity record based on the curation action; 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.