公开(公告)号：US12079282B2

公开(公告)日：2024-09-03

申请号：US16989306

申请日：2020-08-10

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Aras Mumcuyan ,  Iraklis Psaroudakis ,  Miroslav Cepek ,  Rhicheek Patra

IPC: G06F40/00 ,  G06F16/903 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/22 ,  G06F40/30 ,  G06N3/045 ,  G06N3/08 ,  G06N5/04

CPC classification number: G06F16/90344 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/22 ,  G06F40/30 ,  G06N3/045 ,  G06N3/08 ,  G06N5/04

Abstract: Techniques are described herein for a Name Matching Engine that integrates two Machine Learning (ML) module options. The first ML module is a feature-engineered classifier that boosts text-based name matching techniques with a binary classifier ML model. The feature-engineered classifier comprises a first stage of text-based candidate finding, and a second stage in which a binary classifier model predicts whether each string, of the candidate match list, is a match or not. The binary classifier model is based on features from two or more of: a name feature level, a word feature level, a character feature level, and an initial feature level. The second ML module of the Name Matching Engine comprises an end-to-end Recurrent Neural Network (RNN) model that directly accepts name strings as a sequence of n-grams and generates learned text embeddings. The text embeddings of matching name strings are close to each other in the feature space.

公开(公告)号：US20240370500A1

公开(公告)日：2024-11-07

申请号：US18773452

申请日：2024-07-15

Applicant: Oracle International Corporation

Inventor： Aras Mumcuyan ,  Iraklis Psaroudakis ,  Miroslav Cepek ,  Rhicheek Patra

IPC: G06F16/903 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/22 ,  G06F40/30 ,  G06N3/045 ,  G06N3/08 ,  G06N5/04

Abstract: Techniques are described herein for a Name Matching Engine that integrates two Machine Learning (ML) module options. The first ML module is a feature-engineered classifier that boosts text-based name matching techniques with a binary classifier ML model. The feature-engineered classifier comprises a first stage of text-based candidate finding, and a second stage in which a binary classifier model predicts whether each string, of the candidate match list, is a match or not. The binary classifier model is based on features from two or more of: a name feature level, a word feature level, a character feature level, and an initial feature level. The second ML module of the Name Matching Engine comprises an end-to-end Recurrent Neural Network (RNN) model that directly accepts name strings as a sequence of n-grams and generates learned text embeddings. The text embeddings of matching name strings are close to each other in the feature space.

公开(公告)号：US20210287069A1

公开(公告)日：2021-09-16

申请号：US16989306

申请日：2020-08-10

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Aras Mumcuyan ,  Iraklis Psaroudakis ,  Miroslav Cepek ,  Rhicheek Patra

IPC: G06N3/04 ,  G06F16/903 ,  G06K9/62 ,  G06N5/04 ,  G06F40/30

Abstract: Techniques are described herein for a Name Matching Engine that integrates two Machine Learning (ML) module options. The first ML module is a feature-engineered classifier that boosts text-based name matching techniques with a binary classifier ML model. The feature-engineered classifier comprises a first stage of text-based candidate finding, and a second stage in which a binary classifier model predicts whether each string, of the candidate match list, is a match or not. The binary classifier model is based on features from two or more of: a name feature level, a word feature level, a character feature level, and an initial feature level. The second ML module of the Name Matching Engine comprises an end-to-end Recurrent Neural Network (45RNN) model that directly accepts name strings as a sequence of n-grams and generates learned text embeddings. The text embeddings of matching name strings are close to each other in the feature space.