A Novel Approach for Extracting Summarized RDF Graph from Heterogeneous Corpus
Amal Beldi, Jean Raphaël Richa, Salma Sassi, Richard Chbeir, Abderazek Jemai · 2023
Data corpus tend to be heterogeneous presenting a significant challenge in extracting meaningful knowledge from, especially with the rapid growth of digital data. Traditional approaches lack when it comes to handling enormous volumes of unstructured data existing across various sources. Knowledge Graphs, becoming a more and more trendy topic, offer advanced modelization that can help cover such gap. Knowingly, data labeled graph and RDF triplestores are data management approaches that are built on modeling, storing, and querying graph-like data. Despite this fundamental idea, each have unique characteristics that hamper database interoperability. While some methods exist to convert databases to RDF graph or to property graphs and vice versa, they still lack consistency and solid formal foundation. This paper describes Novel Approach for Extracting Summarized RDF Graph from Heterogeneous Corpus.