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.
Authors
- Patrick Westphal (ORCID: https://orcid.org/0000-0002-3855-4485)
- Marcel R. Ackermann (ORCID: https://orcid.org/0000-0001-7644-2495)
- Debayan Banerjee (ORCID: https://orcid.org/0000-0001-7626-8888)
- Ricardo Usbeck (ORCID: https://orcid.org/0000-0002-0191-7211)
- Stefan Ollinger (ORCID: https://orcid.org/0000-0001-6548-5190)
- Tilahun Abedissa Taffa (ORCID: https://orcid.org/0000-0002-2476-8335)
- Patrick Neises (ORCID: https://orcid.org/0000-0002-3419-2544)
Institutions
- Leuphana University of Lüneburg (DE)
- Universität Hamburg (DE)
- Schloss Dagstuhl – Leibniz Center for Informatics (DE)
- Hamburg University of Technology (DE)
Publication Details
- Published
- 2025-12-09
- DOI
- https://doi.org/10.1145/3731443.3771376
- Primary Topic
- Advanced Graph Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00