Generating feature vectors from RDF graphs

A non-transitory computer-readable medium storing computer code for identifying a first document relevant to a topic of interest, the computer code including instructions to cause the processor to:

• produce a Resource Description Framework (RDF) graph of a second document, wherein the RDF graph includes a plurality of nodes and at least one edge node;
• receive the topic of interest to be evaluated;
• determine, based on the RDF graph, key-attributes identified from nodes in the RDF graph responsive to the topic of interest to be evaluated,
• root node-attributes collected from root nodes in the RDF graph pointing to the topic of interest to be evaluated, and
• a Boolean value additional-attribute of interest derived from the identified key-attributes in one of the responsive node or a node connected by a single edge to the responsive node, wherein the Boolean value additional-attribute of interest is one of a true or false feature value;
• generate feature vectors based on a feature value of the identified key-attributes, a feature subject of the root node-attributes, and a determined “true” value of the Boolean value additional-attribute of interest;
• and
• produce, using a machine learning algorithm and the feature vectors, computer instructions configured to identify, in response to a receipt of the first document, that the first document is relevant to the received topic of interest.

A system identifying a first document relevant to a topic of interest, the system comprising:

• a memory configured to store the first document, a second document, a Resource Description Framework (RDF) graph, and feature vectors;
• and
• a processor configured to:
• produce the RDF graph of the second document, wherein the RDF graph includes a plurality of nodes and at least one edge node;
• receive the topic of interest to be evaluated;
• determine, based on the RDF graph, key-attributes identified from nodes in the RDF graph responsive to the topic of interest to be evaluated,
• root node-attributes collected from root nodes in the RDF graph pointing to the topic of interest to be evaluated, and
• a Boolean value additional-attribute of interest derived from the identified key-attributes in one of the responsive node or a node connected by a single edge to the responsive node, wherein the Boolean value additional-attribute of interest is one of a true or false feature value;
• generate feature vectors based on a feature value of the identified key-attributes, a feature subject of the root node-attributes, and a determined “true” value of the Boolean value additional-attribute of interest;
• and
• produce, using a machine learning algorithm and the feature vectors, computer instructions configured to identify, in response to a receipt of the first document, that the first document is relevant to the received topic of interest.