Semantic Parsing for Knowledge Graph Question Answering with Large Language Models

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

This thesis explores the topic of Knowledge Graph Question Answering with a special emphasis on semantic parsing approaches, incorporating pre-trained text-to-text language models. We use the text generation ability of these models to convert natural language questions to logical forms. We test whether correct logical forms are being generated, and if not, how to mitigate the failure cases. As a second step, we try to make the same models generate additional information to aid the process of grounding of the logical forms to entities, relations and literals in the Knowledge Graph. In experiments conducted so far, we see encouraging results on both generation of base logical forms, and grounding them to the KG elements. At the same time, we discover failure cases prompting directions in future work (The author considers himself a ‘middle-stage’ Ph.D. candidate).

Original languageEnglish
Title of host publicationThe Semantic Web : ESWC 2023 Satellite Events, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings
EditorsCatia Pesquita, Hala Skaf-Molli, Vasilis Efthymiou, Sabrina Kirrane, Axel Ngonga, Diego Collarana, Renato Cerqueira, Mehwish Alam, Cassia Trojahn, Sven Hertling
Number of pages10
Place of PublicationCham
PublisherSpringer Science and Business Media Deutschland
Publication date2023
Pages234-243
ISBN (print)978-3-031-43457-0
ISBN (electronic)978-3-031-43458-7
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event20th International Conference on The Semantic Web - ESWC 2023: The Extended Semantic Web Conference - Aldemar Knossos Royal & Royal Villa, Hersonissos, Greece
Duration: 28.05.202301.06.2023
Conference number: 20
https://2023.eswc-conferences.org/

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG. 2023.

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