DBLP QuAD 2.0: Scholarly Natural Questions from SPARQL

We present DBLP-QuAD 2.0, designed to evaluate Scholarly Knowledge Graph Question Answering (KGQA) over DBLP. Recent updates in the underlying DBLP KG, including new entities and relationships such as venues, research streams, and citation links, have necessitated a corresponding update to existing KG QA benchmarking resources. While the DBLP-QuAD dataset focused on author and publication-centered queries, DBLP-QuAD 2.0 broadens the coverage to reflect the enriched structure of the updated KG. Specifically, the questions in our dataset are formulated from SPARQL query logs that cover a wide range of entities involving authors, publications, venues, research streams, and citation relationships. DBLP-QuAD 2.0 thus provides a more comprehensive benchmark for evaluating KGQA systems with a baseline.

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Published
2025-12-09
DOI
https://doi.org/10.1145/3731443.3771376
Primary Topic
Advanced Graph Neural Networks
Type
article
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DBLP QuAD 2.0: Scholarly Natural Questions from SPARQL

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DBLP QuAD 2.0: Scholarly Natural Questions from SPARQL

Patrick Westphal, Marcel R. Ackermann, Debayan Banerjee, Ricardo Usbeck, Stefan Ollinger, Tilahun Abedissa Taffa, Patrick Neises
article en

Abstract

We present DBLP-QuAD 2.0, designed to evaluate Scholarly Knowledge Graph Question Answering (KGQA) over DBLP. Recent updates in the underlying DBLP KG, including new entities and relationships such as venues, research streams, and citation links, have necessitated a corresponding update to existing KG QA benchmarking resources. While the DBLP-QuAD dataset focused on author and publication-centered queries, DBLP-QuAD 2.0 broadens the coverage to reflect the enriched structure of the updated KG. Specifically, the questions in our dataset are formulated from SPARQL query logs that cover a wide range of entities involving authors, publications, venues, research streams, and citation relationships. DBLP-QuAD 2.0 thus provides a more comprehensive benchmark for evaluating KGQA systems with a baseline.

Leuphana University of Lüneburg (DE), Universität Hamburg (DE), Schloss Dagstuhl – Leibniz Center for Informatics (DE), Hamburg University of Technology (DE)
Openalex Percentile: Top 79%
Advanced Graph Neural Networks
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