Abstract: Implementations are directed to receiving current data, processing the current data using a predictive model to provide a result, the result corresponding to a sub-model of the predictive model, determining a set of syntactically similar sub-models based on other data, providing at least one semantic model based on the sub-model of the predictive model, one or more syntactically similar sub-models of the set of syntactically similar sub-models, a domain ontology (knowledge graph), and constraints, the at least one semantic model being provided by merging nodes of the sub-model of the predictive model, and a previously determined sub-model of the predictive model using the domain ontology, a label of the domain ontology being used to label a merged node, determining an interpretation based on the at least one semantic model, the interpretation providing at least one reason for the result, and providing the interpretation.

Abstract: A device may receive user preference data and company preference data associated with one or more bookable items. The device may obtain external preference data associated with the one or more bookable items. The external preference data may be different from the user preference data and the company preference data. The device may generate a list of schedules based on the user preference data, the company preference data, and the external preference data. The device may generate one or more category scores for a plurality of schedules included in the list of schedules based on the company preference data. The device may rank the list of schedules based on the one or more category scores. The device may select a schedule, from the ranked list of schedules, based on the one or more category scores. The device may perform an action based on selecting the schedule.

Abstract: Implementations are directed to receiving a first plurality of data sets associated with one or more of a process and a device, data values in the plurality of data sets being recorded by sensors in a set of sensors, receiving a first predictive model for the first plurality of data sets, for each data value in the first plurality of data sets, determining a knowledge score for the predictive model based on weights assigned to a plurality of concepts associated with a domain ontology for a domain of the one or more of the process and the device, comparing the knowledge score for each data value in the first plurality of data sets to a threshold knowledge score to provide a comparison, and in response to the comparison, selectively amending concepts in the first predictive model to provide a second predictive model.

Abstract: Implementations are directed to receiving current data, processing the current data using a predictive model to provide a result, the result corresponding to a sub-model of the predictive model, determining a set of syntactically similar sub-models based on other data, providing at least one semantic model based on the sub-model of the predictive model, one or more syntactically similar sub-models of the set of syntactically similar sub-models, a domain ontology (knowledge graph), and constraints, the at least one semantic model being provided by merging nodes of the sub-model of the predictive model, and a previously determined sub-model of the predictive model using the domain ontology, a label of the domain ontology being used to label a merged node, determining an interpretation based on the at least one semantic model, the interpretation providing at least one reason for the result, and providing the interpretation.

Abstract: A device may receive user preference data and company preference data associated with one or more bookable items. The device may obtain external preference data associated with the one or more bookable items. The external preference data may be different from the user preference data and the company preference data. The device may generate a list of schedules based on the user preference data, the company preference data, and the external preference data. The device may generate one or more category scores for a plurality of schedules included in the list of schedules based on the company preference data. The device may rank the list of schedules based on the one or more category scores. The device may select a schedule, from the ranked list of schedules, based on the one or more category scores. The device may perform an action based on selecting the schedule.