TY - CHAP

T1 - Modern Baselines for SPARQL Semantic Parsing

AU - Banerjee, Debayan

AU - Nair, Pranav Ajit

AU - Kaur, Jivat Neet

AU - Usbeck, Ricardo

AU - Biemann, Chris

N1 - Conference code: 45

PY - 2022/7/6

Y1 - 2022/7/6

N2 - In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs). We assume that gold entity and relations have been provided, and the remaining task is to arrange them in the right order along with SPARQL vocabulary, and input tokens to produce the correct SPARQL query. Pre-trained Language Models (PLMs) have not been explored in depth on this task so far, so we experiment with BART, T5 and PGNs (Pointer Generator Networks) with BERT embeddings, looking for new baselines in the PLM era for this task, on DBpedia and Wikidata KGs. We show that T5 requires special input tokenisation, but produces state of the art performance on LC-QuAD 1.0 and LC-QuAD 2.0 datasets, and outperforms task-specific models from previous works. Moreover, the methods enable semantic parsing for questions where a part of the input needs to be copied to the output query, thus enabling a new paradigm in KG semantic parsing.

AB - In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs). We assume that gold entity and relations have been provided, and the remaining task is to arrange them in the right order along with SPARQL vocabulary, and input tokens to produce the correct SPARQL query. Pre-trained Language Models (PLMs) have not been explored in depth on this task so far, so we experiment with BART, T5 and PGNs (Pointer Generator Networks) with BERT embeddings, looking for new baselines in the PLM era for this task, on DBpedia and Wikidata KGs. We show that T5 requires special input tokenisation, but produces state of the art performance on LC-QuAD 1.0 and LC-QuAD 2.0 datasets, and outperforms task-specific models from previous works. Moreover, the methods enable semantic parsing for questions where a part of the input needs to be copied to the output query, thus enabling a new paradigm in KG semantic parsing.

KW - knowledge graph

KW - question answering

KW - semantic parsing

KW - sparql

KW - Business informatics

KW - Informatics

UR - http://www.scopus.com/inward/record.url?scp=85135063193&partnerID=8YFLogxK

U2 - 10.1145/3477495.3531841

DO - 10.1145/3477495.3531841

M3 - Article in conference proceedings

AN - SCOPUS:85135063193

T3 - SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

SP - 2260

EP - 2265

BT - SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

A2 - Amigo, Enrique

A2 - Castells, Pablo

A2 - Gonzalo, Julio

PB - Association for Computing Machinery, Inc

T2 - 45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 2022

Y2 - 11 July 2022 through 15 July 2022

ER -