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        <title>A Temporal Knowledge Graph for Music Festival Lineup Forecasting</title>
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            <namePart type="given">Jan-Christoph</namePart>
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    <abstract>Music festival lineups emerge from complex relationships among artists, genres, releases, labels, and past performances, making the prediction of future lineups a natural fit for temporal knowledge graph (TKG) forecasting. In this work, we present a TKG covering 380 festivals over 55 years, comprising more than 90K festival performance quadruples along with information on festivals, artist tours, and artist metadata, and release it as a resource for TKG forecasting evaluation. We formalize festival lineup forecasting as temporal link prediction between artists and festivals at future timestamps. We evaluate six TKG forecasting models on this task, analyze their capabilities and limitations, and compare them against Large Language Models applied zero-shot. Our resource complements existing TKG benchmarks by grounding evaluation in a concrete, real-world application domain.</abstract>
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%0 Conference Proceedings
%T A Temporal Knowledge Graph for Music Festival Lineup Forecasting
%A Gastinger, Julia
%A Dieing, Thilo
%A Meilicke, Christian
%A Stuckenschmidt, Heiner
%Y Kalo, Jan-Christoph
%Y Nakashole, Ndapa
%Y Razniewski, Simon
%Y Suchanek, Fabian M.
%Y Vlachos, Andreas
%Y McCallum, Andrew
%S Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-464-4
%F gastinger-etal-2026-temporal
%X Music festival lineups emerge from complex relationships among artists, genres, releases, labels, and past performances, making the prediction of future lineups a natural fit for temporal knowledge graph (TKG) forecasting. In this work, we present a TKG covering 380 festivals over 55 years, comprising more than 90K festival performance quadruples along with information on festivals, artist tours, and artist metadata, and release it as a resource for TKG forecasting evaluation. We formalize festival lineup forecasting as temporal link prediction between artists and festivals at future timestamps. We evaluate six TKG forecasting models on this task, analyze their capabilities and limitations, and compare them against Large Language Models applied zero-shot. Our resource complements existing TKG benchmarks by grounding evaluation in a concrete, real-world application domain.
%U https://aclanthology.org/2026.akbc-1.4/
%P 26-44

Markdown (Informal)

[A Temporal Knowledge Graph for Music Festival Lineup Forecasting](https://aclanthology.org/2026.akbc-1.4/) (Gastinger et al., AKBC 2026)

• A Temporal Knowledge Graph for Music Festival Lineup Forecasting (Gastinger et al., AKBC 2026)

ACL

• Julia Gastinger, Thilo Dieing, Christian Meilicke, and Heiner Stuckenschmidt. 2026. A Temporal Knowledge Graph for Music Festival Lineup Forecasting. In Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026), pages 26–44, Budapest, Hungary. Association for Computational Linguistics.