A Universal Question-Answering Platform for Knowledge Graphs
Reham Omar, Ishika Dhall, Panos Kalnis, Essam Mansour

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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Advanced Graph Neural Networks
