LEARNED DATA ONTOLOGY USING WORD EMBEDDINGS FROM MULTIPLE DATASETS

Techniques described herein may support a learned ontology or meaning fhr user, organization, or customer specific data. According to the techniques described herein, a set of datasets corresponding to an entity may be processed to generate a master dataset including rows that include at least a fie...

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Bibliographic Details
Main Authors Paul, Thushara, Richey, Behzad Farhang, Kukkar, Sumitkumar, Noonan, Timothy, Zheng, Zuye, Garg, Vaibhav, Tsao, Evan
Format Patent
LanguageEnglish
Published 21.04.2022
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Techniques described herein may support a learned ontology or meaning fhr user, organization, or customer specific data. According to the techniques described herein, a set of datasets corresponding to an entity may be processed to generate a master dataset including rows that include at least a field name and a value corresponding to the field. The master dataset is processed to generate a corpus of text strings that is input into a word embedding function which generates a set of vectors based on the corpus. Because the configuration of the text string positions values by field names and field values, implicit relationships and contexts are identified within the data using the word embedding function.
Bibliography:Application Number: US202017072615