GETT-QA: Graph Embedding Based T2T Transformer for Knowledge Graph Question Answering
Abstract: In this work, we present an end-to-end Knowledge Graph Question Answering (KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text pre-trained language model. The model takes a question in natural language as input and produces a simpler form of the intended SPARQL query. In the simpler form, the model does not directly produce entity and relation IDs. Instead, it produces corresponding entity and relation labels. The labels are grounded to KG entity and relation IDs in a subsequent step. To further improve the results, we instruct the model to produce a truncated version of the KG embedding for each entity. The truncated KG embedding enables a finer search for disambiguation purposes. We find that T5 is able to learn the truncated KG embeddings without any change of loss function, improving KGQA performance. As a result, we report strong results for LC-QuAD 2.0 and SimpleQuestions-Wikidata datasets on end-to-end KGQA over Wikidata.
Show BibTeX
@inproceedings{DBLP:conf/esws/BanerjeeNUB23,
author = {Debayan Banerjee and
Pranav Ajit Nair and
Ricardo Usbeck and
Chris Biemann},
editor = {Catia Pesquita and
Ernesto Jim{\'{e}}nez{-}Ruiz and
Jamie P. McCusker and
Daniel Faria and
Mauro Dragoni and
Anastasia Dimou and
Rapha{\"{e}}l Troncy and
Sven Hertling},
title = {{GETT-QA:} Graph Embedding Based {T2T} Transformer for Knowledge Graph
Question Answering},
booktitle = {The Semantic Web - 20th International Conference, {ESWC} 2023, Hersonissos,
Crete, Greece, May 28 - June 1, 2023, Proceedings},
series = {Lecture Notes in Computer Science},
volume = {13870},
pages = {279--297},
publisher = {Springer},
year = {2023},
url = {https://doi.org/10.1007/978-3-031-33455-9\_17},
doi = {10.1007/978-3-031-33455-9\_17},
timestamp = {Mon, 05 Feb 2024 20:32:24 +0100},
biburl = {https://dblp.org/rec/conf/esws/BanerjeeNUB23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}