Abstract: Disclosed herein are embodiments of systems, methods, and products comprises a server for database search and knowledge mining. The server may learn different table's semantics, relationships, and usage by parsing historical query logs and analyzing tables' metadata (e.g., table descriptions). The analytic server may generate a graph database based on the table relationships obtained from the parsing. The graph database may be a relationship graph where tables are the nodes and edges represent the relationships among tables. When the server receives a query, the server extract semantics of the query, and return a set of tables that are semantically similar to the query. The set of tables may be a list of tables whose semantic similarities with the query satisfies a threshold. The analytic server may further generate a graph including the list of tables to show the relationships of these tables.

Abstract: A server may automatically determine a classification for document text of an electronic document and display a graphical indication of the classification of document text in a first graphical region of a first graphical user interface. In response to the server receiving an approval of the classification, the server may generate a label for the document text based on the classification and train a machine learning (ML) model using the label and the electronic document. Furthermore, the server may execute the trained ML model for a second electronic document. For at least one field of a form on a webpage, the server may automatically complete a widget embedded in the web page, using the trained ML model display a second graphical indication of text in the second electronic document providing data for the at least one field.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises a server for database search and knowledge mining. The server may learn different table's semantics, relationships, and usage by parsing historical query logs and analyzing tables' metadata (e.g., table descriptions). The analytic server may generate a graph database based on the table relationships obtained from the parsing. The graph database may be a relationship graph where tables are the nodes and edges represent the relationships among tables. When the server receives a query, the server extract semantics of the query, and return a set of tables that are semantically similar to the query. The set of tables may be a list of tables whose semantic similarities with the query satisfies a threshold. The analytic server may further generate a graph including the list of tables to show the relationships of these tables.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises a server for database search and knowledge mining. The server may learn different table's semantics, relationships, and usage by parsing historical query logs and analyzing tables' metadata (e.g., table descriptions). The analytic server may generate a graph database based on the table relationships obtained from the parsing. The graph database may be a relationship graph where tables are the nodes and edges represent the relationships among tables. When the server receives a query, the server extract semantics of the query, and return a set of tables that are semantically similar to the query. The set of tables may be a list of tables whose semantic similarities with the query satisfies a threshold. The analytic server may further generate a graph including the list of tables to show the relationships of these tables.