Question answering over knowledge graphs using BERT based relation mapping

Abstract

AbstractA knowledge graph (KG) is a structured form of knowledge describing real‐world entities, properties and relationships as a graph. Question answering over knowledge graphs (KGQA) allows people to ask questions in natural language and extract answers from KG accurately and more quickly. The main task of a KGQA is to convert a natural language query to the corresponding structured query form like SPARQL. However, generating the precise SPARQL query from a question is challenging and highly error‐prone. Here we propose a question‐answering framework that uses KG to answer simple questions without using SPARQL. Question classification, dependency parsing, entity linking, BERT‐based relation finding and answer extraction constitute the main modules of the approach. We have used the DBpedia as the KG and tested the end‐to‐end system with a subset of QALD‐4, LC‐QuAD and SimpleQuestions datasets. Results show considerable improvement compared to other approaches in terms of F1‐score.