Infosys Limited

Megha Honna

Rajeshwari Ganesan

Jasleen Singh

Pratheeksha Maddi

A method and system for generation of queries by machine learning (ML) models is provided. An ML model may generate a query based on reception of a data trigger. The query may be generated based on corresponding domain and a knowledge graph. The ML model may receive a response in an encoded format. The knowledge graph may evolve based on the response. A first subsequent query may be generated by the ML model based on the response and the evolved knowledge graph. The ML model may receive a response for the subsequent query in the encoded format. The ML model may determine whether the response culminates a current iteration. A second subsequent query may be generated by the ML model when the response does not culminate the current iteration. The current iteration may be terminated when it is determined that the response culminates the current iteration.

System and method for generating queries by machine learning models

A method for transforming data organized in tabular structure is disclosed. In some embodiments, the method includes assigning a score to each of a plurality of cells within a table based on an associated set of orthogonal features characterizing a set of data. The set of orthogonal features comprises visual features, syntactic features, and language-based features. The method further includes identifying for each of the plurality of cells, a cell type based on the assigned score. The method further includes determining a table type based on the cell type and the set of orthogonal features determined for each of the plurality of cells. The table type comprises one of a row-oriented table, a column-oriented table, or a composite table.

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Cary, NC, United States of America

Megha Honna