Key-value information extraction from unstructured data

A system configured to extract key-value attributes from unstructured data, comprising:

• memory comprising a set of instructions;
• and
• at least one processor, wherein
• the set of instructions are configured to cause the at least one processor to execute:
• receiving, by a parser module, a first incident comprising key-value attributes and a second incident comprising key-value attributes, wherein the first incident and the second incident refer to an unforeseen event or issue affecting performance and functionality of an information technology (IT) system, service, or application;
• parsing, by the parser module, the first incident for the key-value attributes and the second incident for key-value attributes;
• performing, by a machine learning (ML) model, a pairwise comparison of one or more common key-value attributes associated with the first incident and the second incident;
• computing, by a cosine similarity module, a cosine similarity between the one or more common key-value attributes to generate a score for each of the one or more common key-value attributes associated with the first incident and the second incident;
• generating, by the cosine similarity module, a final score between the first incident and the second incident by averaging all cosine similarity scores computed for each of the one or more common key-value attributes;
• and
• performing a recommendation function by ranking each incident and identifying each incident with a score in top five as related incidents for management and resolution of each incident, and resolving one of the incidents from the top five.

A non-transitory computer-readable medium comprising a computer program configured to extract key-value attributes from unstructured data, the computer program being configured to cause at least one processor to execute:

• receiving, by a parser module, a first incident comprising key-value attributes and a second incident comprising key-value attributes, wherein the first incident and the second incident refer to an unforeseen event or issue affecting performance and functionality of an information technology (IT) system, service, or application;
• parsing, by the parser module, the first incident for the key-value attributes and the second incident for key-value attributes;
• performing, by a machine learning (ML) model, a pairwise comparison of one or more common key-value attributes associated with the first incident and the second incident;
• computing, by a cosine similarity module, a cosine similarity between the one or more common key-value attributes to generate a score for each of the one or more common key-value attributes associated with the first incident and the second incident;
• generating, by the cosine similarity module, a final score between the first incident and the second incident by averaging all cosine similarity scores computed for each of the one or more common key-value attributes;
• and
• performing a recommendation function by ranking each incident and identifying each incident with a score in top five as related incidents for management and resolution of each incident, and resolving one of the incidents from the top five.