Semantic and audiovisual analysis techniques

A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:

• semantically analyzing a first set of data for a matter in order to extract a plurality of features, wherein the plurality of features includes a plurality of subjects represented in the first set of data, wherein semantically analyzing the first set of data includes applying a machine learning model;
• consolidating the first set of data into a knowledge base based on the extracted plurality of features, wherein the knowledge base includes a graph having a plurality of nodes and a plurality of edges, wherein the plurality of nodes represent the plurality of subjects, wherein the plurality of edges represent connections among the plurality of subjects and are between pairs of nodes among the plurality of nodes;
• querying the knowledge base based on a second set of data in order to obtain knowledge base query results, wherein querying the knowledge base includes semantically analyzing the second set of data in order to identify at least one subject of the plurality of subjects represented in the second set of data, wherein semantically analyzing the second set of data includes applying the machine learning model;
• and
• validating at least a portion of the second set of data based on the knowledge base query results.

A system for inconsistency detection, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• semantically analyze a first set of data for a matter in order to extract a plurality of features, wherein the plurality of features includes a plurality of subjects represented in the first set of data, wherein semantically analyzing the first set of data includes applying a machine learning model;
• consolidate the first set of data into a knowledge base based on the extracted plurality of features, wherein the knowledge base includes a graph having a plurality of nodes and a plurality of edges, wherein the plurality of nodes represent the plurality of subjects, wherein the plurality of edges represent connections among the plurality of subjects and are between pairs of nodes among the plurality of nodes;
• query the knowledge base based on a second set of data in order to obtain knowledge base query results, wherein querying the knowledge base includes semantically analyzing the second set of data in order to identify at least one subject of the plurality of subjects represented in the second set of data, wherein semantically analyzing the second set of data includes applying the machine learning model;
• and
• validate at least a portion of the second set of data based on the knowledge base query results.