A Temporal Knowledge Graph for Music Festival Lineup Forecasting

2026-09-21

AI Summary

本文构建了一个覆盖380个音乐节的时序知识图谱，用于预测未来音乐节阵容，并评估了六种时序知识图谱预测模型。

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.

Problem

• Music Festival
• Lineup Forecasting
• Temporal Knowledge Graph

Innovation

• Temporal Knowledge Graph
• Music Festival Lineup Forecasting
• Temporal Link Prediction