Oracle International Corporation

Business Objects Software Limited

Abhiram Madhukar Gujjewar

Ganesh Seetharaman

Gopal Srinivasa Raghavan

Manasjyoti Sharma

Jai Motwani

Sayon Dutta

Rajat Mahajan

Jeffrey Woody

Mark Spiess

A machine-learning model may be previously trained with a supervised learning algorithm to identify whether a pair of labels provided as input are similar. A locality sensitive hashing forest (LSH) may be generated for the set of candidate labels. When a user later identifies an input label (e.g., by search query, by interface selection, etc.) the input label may be used to query the LSH forest to identify a subset of the candidate labels. This subset may be used to generate respective pairs comprising the input label, one of the subset candidate labels, and a corresponding feature set generated for the pair. This data may be provided to the model to identify a degree to which the pair of labels are similar. The user may be provided one or more recommendations including similar terms identified from the model's output.

Techniques for linking data to provide improved searching capabilities

Systems and methods for matching data based on numeric difference are described herein. Input data elements are parsed to identify a first number and a second number. A difference between the first number and the second number is calculated based on a predefined formula. Based on the difference, a matching score between the input data elements is evaluated. The matching score is proportional to a base matching score corresponding to a threshold difference, and a maximum score corresponding to a match between the first number and the second number. A similarity between the input data elements is reported based on the evaluated matching score.

Matching data based on numeric difference

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San Ramon, CA, United States of America

Abhiram Madhukar Gujjewar