Nominated for Best Paper at SEMANTiCS 2021Knowledge Graph Question Answering using Graph-Pattern Isomorphism

12 July 2021, by David Mosteller

Photo: Ricardo Usbeck/SEMS

We are happy to announce that our paper entitled “Knowledge Graph Question Answering using Graph-Pattern Isomorphism" has been nominated for the “Best Paper Award” in SEMANTiCs 2021. The final decision will be made during the conference after visiting the presentations of each of the nominated papers

Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. 

Preprint: https://arxiv.org/abs/2103.06752 

Demo: https://tebaqa.demos.dice-research.org/ 

Acknowledgment: This is joint between Fraunhofer IAIS, https://dice-research.org/, and SEMS.