QART: A Framework to Transform Natural Language Questions and Answers into RDF Triples

André Gomes Regino, Rodrigo Oliveira Caus, Victor Hochgreb, Júlio Cesar dos Reis

Abstract: Knowledge Graphs (KGs) model real-world things and their interactions. Several software systems have recently adopted the use of KGs to improve their data handling. E-commerce platforms are examples of software exploring the power of KGs in diversified tasks, such as advertisement and product recommendation. In this context, generating trustful, meaningful and scalable RDF triples for populating KGs remains an arduous and error-prone task. The automatic insertion of new knowledge in e-commerce KGs is highly dependent on data quality, which is often not available. In this article, we propose a framework for generating RDF triple knowledge from natural language texts. The QART framework is suited to extract knowledge from Q&A regarding e-commerce products and generate triples associated with it. QART produces KG triples reliable to answer similar questions in an e-commerce context. We evaluate one of the key steps in QART to generate summary sentences and identify product Q&A intents a