Abstract: A device may generate, from a subset of historical ontology data and a substance description of a substance, a knowledge base. The subset of historical ontology data may be associated with historical substances. The device may generate, based on the knowledge base, a substance knowledge graph embedding (KGE) that is representative of the substance; compare the substance KGE and a historical KGE associated with the knowledge base; determine, based on comparing the substance KGE and the historical KGE, a similarity score associated with the substance KGE and the historical KGE; determine, based on the similarity score, whether substance data associated with a related substance is similarly represented in the substance KGE and the historical KGE; and perform, based on whether the substance data is similarly represented in the substance KGE and the historical KGE, an action associated with the related substance relative to the substance description or the knowledge base.

Abstract: A device may generate, from a subset of historical ontology data and a substance description of a substance, a knowledge base. The subset of historical ontology data may be associated with historical substances. The device may generate, based on the knowledge base, a substance knowledge graph embedding (KGE) that is representative of the substance; compare the substance KGE and a historical KGE associated with the knowledge base; determine, based on comparing the substance KGE and the historical KGE, a similarity score associated with the substance KGE and the historical KGE; determine, based on the similarity score, whether substance data associated with a related substance is similarly represented in the substance KGE and the historical KGE; and perform, based on whether the substance data is similarly represented in the substance KGE and the historical KGE, an action associated with the related substance relative to the substance description or the knowledge base.

Abstract: An example implementation described herein involves identifying an artificial intelligence module to train a user; selecting, using the artificial intelligence module, a set of tasks from a plurality of tasks to provide to the user; providing the set of tasks to the user; monitoring a performance parameter associated with the user performing the tasks; identifying a machine learning model to determine a level of expertise of the user; determining, using the performance parameter as an input to the machine learning model, whether the level of expertise of the user satisfies an expertise threshold; obtaining a configuration update to the artificial intelligence module from the user, determining that the level of expertise of the user satisfies the expertise threshold; and updating the artificial intelligence module to use the configuration update in association with training one or more users or selecting a subsequent set of tasks from the plurality of tasks based on determining that the level of expertise of t

Abstract: An example implementation described herein involves identifying an artificial intelligence module to train a user; selecting, using the artificial intelligence module, a set of tasks from a plurality of tasks to provide to the user; providing the set of tasks to the user; monitoring a performance parameter associated with the user performing the tasks; identifying a machine learning model to determine a level of expertise of the user; determining, using the performance parameter as an input to the machine learning model, whether the level of expertise of the user satisfies an expertise threshold; obtaining a configuration update to the artificial intelligence module from the user, determining that the level of expertise of the user satisfies the expertise threshold; and updating the artificial intelligence module to use the configuration update in association with training one or more users or selecting a subsequent set of tasks from the plurality of tasks based on determining that the level of expertise of t