Knowledge graph creation utilizing embedding and large language models

A method of providing one or more recommendations comprising:

• querying a knowledge graph to generate the one or more recommendations, wherein the knowledge graph comprises:
• a plurality of item nodes associated with a plurality of items, wherein:
• each respective item, of the plurality of items, are associated with a respective industry of one or more industries;
• and
• each respective item associated with the respective industry comprises a unique item-industry pair;
• one or more industry nodes associated with the one or more industries;
• and
• a plurality of edges, wherein:
• each respective edge of the plurality of edges connects a respective pair of items nodes of the plurality of item nodes, and
• each respective edge indicating that the respective items associated with the respective pair of item nodes comprise:
• associated items in a same industry of the one or more industries that are associated, or
• associated items in different industries of the one or more industries;
• and
• providing the one or more recommendations.

A processing system, comprising:

• a memory comprising computer-executable instructions;
• and
• a processor configured to execute the computer-executable instructions and cause the processing system to:
• for each respective item, of a plurality of items, associated with a respective industry of one or more industries:
• add an item node in a knowledge graph for the respective item;
• add an industry node in the knowledge graph for the respective industry associated with the respective item if no industry node for the respective industry exists in the knowledge graph;
• generate a set of semantically similar items to the respective item;
• and
• prompt one or more machine learning models to determine that the respective item and at least one semantically similar item of the set of semantically similar items are associated;
• and
• generate an edge between the respective item and the at least one semantically similar item based on the association determination.