Abstract: The present disclosure involves systems, software, and computer implemented methods for contextualizing data to augment processes using semantic technologies and artificial intelligence. One example method includes identifying one or more data sources for semantic analysis. The data sources can include a data warehouse, a database, or a data lake. User behaviors of one or more users are identified for semantic analysis. The user behaviors include behaviors of how the users consume data in the one or more data sources. A semantic model is generated, using a knowledge graph, for the user behaviors. Nodes of the knowledge graph correspond to a class of entities in the data sources and are annotated with user behaviors and data source information. One or more queries from a user are monitored. Data from at least one node of the knowledge graph is recommended to the user, based on the semantic model and the queries.

Abstract: The present disclosure involves systems, software, and computer implemented methods for contextualizing data to augment processes using semantic technologies and artificial intelligence. One example method includes identifying one or more data sources for semantic analysis. The data sources can include a data warehouse, a database, or a data lake. User behaviors of one or more users are identified for semantic analysis. The user behaviors include behaviors of how the users consume data in the one or more data sources. A semantic model is generated, using a knowledge graph, for the user behaviors. Nodes of the knowledge graph correspond to a class of entities in the data sources and are annotated with user behaviors and data source information. One or more queries from a user are monitored. Data from at least one node of the knowledge graph is recommended to the user, based on the semantic model and the queries.