Generating and presenting a searchable graph based on a graph query

A system, comprising:

• one or more processors;
• memory in electronic communication with the one or more processors;
• instructions stored in the memory, the instructions being executable by the one or more processors to:
• identify a collection of digital content items;
• apply an entity detection model to the collection of digital content items to identify a subset of digital content items from the collection of digital content items that includes one or more entities of interest within visual content of the subset of digital content items, wherein the entity detection model is trained to detect the one or more entities of interest within a given digital content item based on a combination of metadata and visual content of the given digital content item;
• provide the subset of digital content items as inputs to a concept detection model, the concept detection model being trained to predict whether one or more concepts from a collection of concepts exists within an input digital content item;
• receive a set of concept identifiers for the subset of digital content items output by the concept detection model, the set of concept identifiers indicating a plurality of concepts estimated to be present within the subset of digital content items;
• determine correlation values for a plurality of correlations indicating frequencies of co-occurrences between each entity of interest from the one or more entities of interest and each concept from the plurality of concepts;
• and
• generate a correlation graph object for the subset of digital content items, the correlation graph object including a searchable matrix including the set of concept identifiers, the one or more entities of interest, and a subset of correlations from the plurality of correlations having correlation values indicating frequency of co-occurrence between respective entities of interest and respective content identifiers within the subset of digital content items, the subset of correlations excluding a first one or more correlations having correlation scores below a minimum threshold value and a second one or more correlations having correlation scores above a maximum threshold value.

A non-transitory computer readable medium storing instructions thereon that, when executed by one or more processors, causes a computing device to:

• identify a collection of digital content items;
• apply an entity detection model to the collection of digital content items to identify a subset of digital content items from the collection of digital content items that includes one or more entities of interest within visual content of the subset of digital content items, wherein the entity detection model is trained to detect the one or more entities of interest within a given digital content item based on a combination of metadata and visual content of the given digital content item;
• provide the subset of digital content items as inputs to a concept detection model, the concept detection model being trained to predict whether one or more concepts from a collection of concepts exists within an input digital content item;
• receive a set of concept identifiers for the subset of digital content items output by the concept detection model, the set of concept identifiers indicating a plurality of concepts estimated to be present within the subset of digital content items;
• determine correlation values for a plurality of correlations indicating frequencies of co-occurrences between each entity of interest from the one or more entities of interest and each concept from the plurality of concepts;
• and
• generate a correlation graph object for the subset of digital content items, the correlation graph object including a searchable matrix including the set of concept identifiers, the one or more entities of interest, and a subset of correlations from the plurality of correlations having correlation values indicating frequency of co-occurrence between respective entities of interest and respective content identifiers within the subset of digital content items, the subset of correlations excluding a first one or more correlations having correlation scores below a minimum threshold value and a second one or more correlations having correlation scores above a maximum threshold value.