Knowledge Graph Generation for Unstructured Data Using Data Processing Pipeline

The proliferation of technologies and unstructured data on the internet poses a persistent challenge in extracting valuable information from diverse formats. To address this, research leverages Machine Learning (ML) and Natural Language Processing (NLP) techniques. This study contributes to information extraction from unstructured text using a state-of-the-art pipeline, incorporating modules for coreference resolution (Neuralcoref), named entity linking (Wikifier API), and Relationship Extraction (RE) (OpenNRE and REBEL models). The resulting Knowledge Graph (KG) in Neo4j captures entity relationships. Experiments on a BBC news dataset analyzed the pipeline’s performance, focusing on RE. Accuracies of 61.4% (OpenNRE) and 87% (REBEL) were achieved. The research demonstrates the efficacy of the proposed pipeline in extracting structured knowledge from unstructured data, facilitating the preservation and utilization of valuable information.