Systems and methods for developing and implementing knowledge graphs using large language models

A method, the method comprising:

• receiving, with a processing system including one or more electronic processors, a first electronic content including a plurality of content portions that are arranged within the first electronic content in a sequence;
• generating, with the processing system, a plurality of nodes for the first electronic content, wherein each node of the plurality of nodes corresponds to one of the plurality of content portions of the first electronic content;
• determining, with the processing system, a plurality of node pairings from the plurality of nodes;
• executing, with the processing system, using a large language model (LLM), a first LLM query to determine a metric for each of the plurality of node pairings, wherein the metric indicates a degree of dependency for a respective node pairing of the plurality of node pairings;
• receiving, with the processing system, a response to the first LLM query, wherein the response indicates the metric for each of the plurality of node pairings;
• and
• generating, with the processing system, a graphical representation based on the metric for each of the plurality of node pairings, wherein the graphical representation indicates requisite relationships between the plurality of content portions of the first electronic content.

A non-transitory, computer-readable medium storing instructions that, when executed by an electronic processor, perform a set of functions, the set of functions comprising:

• receiving electronic content that includes a plurality of content portions arranged within the electronic content in a sequential order, wherein the plurality of content portions includes a first content portion pertaining to a first topic and a second content portion pertaining to a second topic;
• identifying a plurality of nodes for the electronic content, wherein the plurality of nodes includes a first node representing a first content portion of the plurality of content portions and a second node representing a second content portion of the plurality of content portions;
• determining, from the plurality of nodes, a plurality of node pairings based on the sequential order of the electronic content, wherein the plurality of node pairings includes a first node pairing including the first node and the second node;
• determining, using a large language model (LLM), a plurality of metrics based on the plurality of node pairings, wherein the plurality of metrics includes a first metric for the first node pairing, wherein the first metric indicates a requisite relationship between the first topic and the second topic;
• and
• generating a graphical representation for the electronic content based on the plurality of metrics, wherein the graphical representation indicates the requisite relationship between the first topic and the second topic such that the graphical representation indicates that the first topic is a prerequisite to the second topic.