A Temporal Knowledge Graph for Music Festival Lineup Forecasting

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.

Machine Learning (cs.LG)

arXiv:2609.24467 [cs.LG]

(or arXiv:2609.24467v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.24467 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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