US 12,266,077 B2
Deep learning of entity resolution rules
Sheshera Mysore, Sunderland, MA (US); Sairam Gurajada, San Jose, CA (US); Lucian Popa, San Jose, CA (US); Kun Qian, San Jose, CA (US); and Prithviraj Sen, San Jose, CA (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Dec. 14, 2020, as Appl. No. 17/120,974.
Prior Publication US 2022/0188974 A1, Jun. 16, 2022
Int. Cl. G06N 20/00 (2019.01); G06N 3/08 (2023.01); G06N 5/025 (2023.01); G06T 3/4046 (2024.01); G06F 16/215 (2019.01); G06F 40/295 (2020.01); G06N 3/042 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01)
CPC G06T 3/4046 (2013.01) [G06N 3/08 (2013.01); G06N 5/025 (2013.01); G06F 16/215 (2019.01); G06F 40/295 (2020.01); G06N 3/042 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving historical pairs of entities including labels indicating whether the historical pairs of entities match;
determining a set of rules for determining whether a pair of entities are matching using the historical pairs of entities, wherein the set of rules comprises a plurality of conditions, each condition representing an instance where the pair of entities could be matching;
building and training a deep neural network using the historical pairs of entities, wherein similarity functions of the historical pairs of entities are input into an embedding layer of the deep neural network, and the deep neural network learns at least predicate parameters and types of conjunctions for the similarity functions and forms entity resolution rules based on the similarity functions and corresponding predicate parameters and types of conjunctions;
developing, using the deep neural network, an entity resolution model based on the historical pairs of entities and the entity resolution rules, wherein the deep neural network comprise a recurrent neural network, and wherein the recurrent neural network is used to determine similarity metrics for the historical pairs of entities in the entity resolution model and analyze the historical pairs of entities and learn similarity metrics from the analysis;
responsive to entity resolution model being developed, receiving, by the entity resolution model, a new pair of entities; and
applying the entity resolution model to the new pair of entities, wherein applying the entity resolution model comprises:
determining whether the one or more conditions and the entity resolution rules associated with the entity resolution model are satisfied by the new pair of entities; and
categorizing the new pair of entities as matching or not matching, based on whether the one or more rules are satisfied.