Key-value information extraction from unstructured data

Abstract

Extracting key-value attributes from unstructured data includes a parser receiving a first incident comprising key-value attributes and a second incident comprising key-value attributes, and parsing the first incident for the key-value attributes and the second incident for key-value attributes. A machine learning (ML) model perform a pairwise comparison of one or more common key-value attributes associated with the first incident and the second incident. A cosine similarity module computes 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, and generates 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.