In an example, a first set of text may be received from a client device. A set of content items may be selected from among content items based upon the first set of text and a plurality of sets of content item text associated with the content items. A set of terms may be determined based upon the first set of text and the set of content items. A similarity profile associated with the set of terms may be generated. The similarity profile is indicative of similarity scores associated with similarities between terms of the set of terms. Relevance scores associated with the set of terms may be determined based upon the similarity profile. One or more search terms may be selected from among the set of terms based upon the relevance scores. A search may be performed based upon the one or more search terms.

In some implementations, a device may monitor a set of data sources to generate a set of language models corresponding to the set of data sources. The device may determine a plurality of sets of keyword groups. The device may generate a plurality of sets of skill catalogs. The device may receive a source document for processing. The device may process the source document to extract a key phrase set and to determine a first similarity distance. The device may select a corresponding skill catalog and an associated language model based on a relevancy value. The device may determine second similarity distances between the source document and one or more target documents using the corresponding skill catalog and the associated language model. The device may output information associated with one or more target documents based at least in part on the second similarity distances.

In some implementations, a device may monitor a set of data sources to generate a set of language models corresponding to the set of data sources. The device may determine a plurality of sets of keyword groups. The device may generate a plurality of sets of skill catalogs. The device may receive a source document for processing. The device may process the source document to extract a key phrase set and to determine a first similarity distance. The device may select a corresponding skill catalog and an associated language model based on a relevancy value. The device may determine second similarity distances between the source document and one or more target documents using the corresponding skill catalog and the associated language model. The device may output information associated with one or more target documents based at least in part on the second similarity distances.

A system, method, and apparatus for implementing workflows across multiple differing systems and devices are provided herein. During operation a workflow is automatically generated upon the detection of new signage. In particular, a workflow server will detect the presence of new signage in a particular area. The new signage will be analyzed, and an appropriate trigger and action will be determined based on the new signage. The appropriate trigger and action will then be implemented as a newly-created workflow.

A system, method, and apparatus for implementing workflows across multiple differing systems and devices are provided herein. During operation a workflow is automatically generated upon the detection of new signage. In particular, a workflow server will detect the presence of new signage in a particular area. The new signage will be analyzed, and an appropriate trigger and action will be determined based on the new signage. The appropriate trigger and action will then be implemented as a newly-created workflow.

Techniques for phased deployment of machine learning models are described. Customers can call a training API to initiate model training, but then must wait while the training completes before the model can be used to perform inference. Depending on the type of model, machine learning algorithm being used for training, size of the training dataset, etc. this training process may take hours or days to complete. This leads to significant downtime where inference requests cannot be served. Embodiments improve upon existing systems by providing phased deployment of custom models. For example, a simple, less accurate model, can be provided synchronously in response to a request for a custom model. At the same time, one or more machine learning models can be trained asynchronously in the background. When the machine learning model is ready for use, the customers' traffic and jobs can be transferred over to the better model.

Techniques for phased deployment of machine learning models are described. Customers can call a training API to initiate model training, but then must wait while the training completes before the model can be used to perform inference. Depending on the type of model, machine learning algorithm being used for training, size of the training dataset, etc. this training process may take hours or days to complete. This leads to significant downtime where inference requests cannot be served. Embodiments improve upon existing systems by providing phased deployment of custom models. For example, a simple, less accurate model, can be provided synchronously in response to a request for a custom model. At the same time, one or more machine learning models can be trained asynchronously in the background. When the machine learning model is ready for use, the customers' traffic and jobs can be transferred over to the better model.

A method may include presenting a user interface on a display device of a computing device, the user interface including: a search query input element; a plurality of graph type options; a graph level selection element; and a graph presentation area; receiving a search query inputted into the search query input element, the search query identifying a concept object in an ontology; retrieving data associated with the concept object from a graph database based on selections made in the graph type options and the graph level selection element, the data including a set of result objects related to the concept object; and rendering a hierarchical graph in the graph presentation area, the hierarchical graph illustrating the set of result objects and the concept object as interactive nodes.

A method may include presenting a user interface on a display device of a computing device, the user interface including: a search query input element; a plurality of graph type options; a graph level selection element; and a graph presentation area; receiving a search query inputted into the search query input element, the search query identifying a concept object in an ontology; retrieving data associated with the concept object from a graph database based on selections made in the graph type options and the graph level selection element, the data including a set of result objects related to the concept object; and rendering a hierarchical graph in the graph presentation area, the hierarchical graph illustrating the set of result objects and the concept object as interactive nodes.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.