A Temporal Knowledge Graph for Music Festival Lineup Forecasting
cs.LG
Submitted: 2026-09-21
Updated: 2026-09-21
Comments: Accepted to 11th Workshop on Automated Knowledge Base Construction (AKBC) 2026
Code: https://github.com/JuliaGast/festival-lineup-tkg
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: 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
Terminology
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.
Sources
- CountTRuCoLa: Rule Learning for Interpretable Temporal Knowledge Graph Forecasting
- Mixtral of Experts
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