Systems and methods for generating a knowledge graph including interaction information

A non-transitory computer readable storage medium having computer readable program code stored thereon for causing a computer system to perform a method for generating a knowledge graph of interaction information, the method comprising:

• receiving a data item comprising an interaction between at least two entities;
• generating, at a first large language model, a knowledge tuple for the data item, the knowledge tuple comprising entities of the data item and a topic of the interaction of the data item;
• identifying at least one thread to which the data item corresponds, wherein a thread comprises connected data items;
• generating, at a second large language model, an interaction tuple for the data item, the interaction tuple comprising the entities of the data item, the thread corresponding to the data item, and a type of interaction of the data item, wherein the generating, at the second large language model, the interaction tuple for the data item comprises:
• extracting, at the second large language model, the entities of the data item;
• receiving the at least one thread to which the data item corresponds;
• and
• classifying the data item according to the type of interaction of the data item;
• and
• populating the knowledge graph the knowledge tuple and the interaction tuple, the knowledge graph comprising nodes associated with the entities, the topics, and the interactions, wherein the nodes associated with interactions comprise the type of interaction and thread of the data item.

A computer-implemented method for generating a knowledge graph of interaction information, the method comprising:

• receiving a plurality of data items comprising a data item, wherein the data item of the plurality of data items comprises an interaction between at least two entities;
• determining at least one domain for the data item;
• provided the at least one domain for the data item corresponds to a domain of a knowledge graph:
• generating, at a first large language model, a knowledge tuple for the data item, the knowledge tuple comprising entities of the data item and a topic of the interaction of the data item;
• identifying at least one thread to which the data item corresponds, wherein a thread comprises connected data items;
• generating, at a second large language model, an interaction tuple for the data item, the interaction tuple comprising the entities of the data item, the thread corresponding to the data item, and a type of interaction of the data item;
• and
• populating the knowledge graph the knowledge tuple and the interaction tuple, the knowledge graph comprising nodes associated with the entities, the topics, and the interactions, wherein the nodes associated with interactions comprise the type of interaction and thread of the data item, and wherein the knowledge graph is populated with a plurality of knowledge tuples and a plurality of interaction tuples corresponding to the domain of the knowledge graph.