Automated annotation of heterogeneous content

A system comprising:

• a computing platform including a hardware processor and a system memory storing a software code and a content labeling predictive model;
• the hardware processor configured to execute the software code to:
• scan a database to identify a plurality of content assets stored in the database;
• parse metadata stored in the database to identify a plurality of labels associated with the plurality of identified content assets;
• generate graph data by creating a plurality of first links linking each one of the plurality of identified content assets to each of the plurality of identified labels associated with a corresponding one of the plurality of identified content assets;
• cluster the graph data in an unsupervised learning process;
• route the graph data to at least one classifier network based on the clustering;
• use the at least one classifier network to generate training data for the content labeling predictive model using the graph data;
• and
• train, using a training dataset including the training data generated by the at least one classifier network, the content labeling predictive model.

A method comprising:

• scanning a database to identify a plurality of content assets stored in the database;
• parsing metadata stored in the database to identify a plurality of labels associated with the plurality of identified content assets;
• generating a graph by creating a plurality of first links linking each one of the plurality of identified content assets to each of the plurality of identified labels associated with a corresponding one of the plurality of identified content assets;
• training, based on the graph, a content labeling predictive model;
• identifying, using the trained content labeling predictive model, a plurality of second links among the plurality of identified content assets and the plurality of identified labels;
• and
• annotating the plurality of identified content assets based on the plurality of identified second links.

A method comprising:

• scanning a database to identify a plurality of content assets stored in the database;
• parsing metadata stored in the database to identify a plurality of labels associated with the plurality of identified content assets;
• generating a graph by creating a plurality of first links linking each one of the plurality of identified content assets to each of the plurality of identified labels associated with a corresponding one of the plurality of identified content assets;
• clustering nodes of the graph in an unsupervised learning process to generate clusters of nodes;
• labeling the clusters of nodes using one or more classification networks to generate cluster labels;
• updating the graph by linking the clusters of nodes to the cluster labels;
• and
• training, based on the graph, a content labeling predictive model.

A system comprising:

• a computing platform including a hardware processor and a system memory storing a search engine and a graph of a plurality of content assets;
• the hardware processor configured to execute the search engine to:
• receive a search term as an input from a user of the system;
• traverse the graph using the search term to discover a content asset corresponding to the search term, wherein the discovered content asset is one of a plurality of content assets annotated using a content labeling predictive model;
• and
• output a search result identifying the discovered content;
• wherein the plurality of content assets are annotated by:
• scanning a database to identify the plurality of content assets stored in the database;
• parsing metadata stored in the database to identify a plurality of labels associated with the plurality of identified content assets;
• generating graph data by creating a plurality of first links linking each one of the plurality of identified content assets to each of the plurality of identified labels associated with a corresponding one of the plurality of identified content assets;
• training, based on the graph data, the content labeling predictive model;
• identifying, using the trained content labeling predictive model, a plurality of second links among the plurality of identified content assets and the plurality of identified labels;
• and
• annotating the plurality of identified content assets based on the plurality of identified second links.