A Universal Question-Answering Platform for Knowledge Graphs

TL;DR

KGQAn is a universal question-answering system for knowledge graphs that translates natural language questions into SPARQL queries without KG-specific tuning, outperforming existing methods in quality and speed.

Contribution

The paper introduces KGQAn, a novel universal QA system that uses a neural text generation approach and a just-in-time linker to handle diverse KGs without prior customization.

Findings

Outperforms state-of-the-art QA systems in answer quality

Achieves faster processing times on various KGs

Easily deployable without pre-processing or KG-specific training

Abstract

Knowledge from diverse application domains is organized as knowledge graphs (KGs) that are stored in RDF engines accessible in the web via SPARQL endpoints. Expressing a well-formed SPARQL query requires information about the graph structure and the exact URIs of its components, which is impractical for the average user. Question answering (QA) systems assist by translating natural language questions to SPARQL. Existing QA systems are typically based on application-specific human-curated rules, or require prior information, expensive pre-processing and model adaptation for each targeted KG. Therefore, they are hard to generalize to a broad set of applications and KGs. In this paper, we propose KGQAn, a universal QA system that does not need to be tailored to each target KG. Instead of curated rules, KGQAn introduces a novel formalization of question understanding as a text generation problem to convert a question into an intermediate abstract representation via a neural sequence-to-sequence model. We also develop a just-in-time linker that maps at query time the abstract representation to a SPARQL query for a specific KG, using only the publicly accessible APIs and the existing indices of the RDF store, without requiring any pre-processing. Our experiments with several real KGs demonstrate that KGQAn is easily deployed and outperforms by a large margin the state-of-the-art in terms of quality of answers and processing time, especially for arbitrary KGs, unseen during the training.