用时序知识图谱预测音乐节阵容：55年数据集开源
A Temporal Knowledge Graph for Music Festival Lineup Forecasting
有人把 55 年音乐节阵容数据做成时序知识图谱，拿来预测未来阵容，还和大模型零样本对比了一波。
研究者构建了一个覆盖 55 年、380 个音乐节的时序知识图谱（TKG），包含超过 9 万条音乐节演出四元组，以及艺人巡演和艺人元数据信息，作为公开评测资源发布。该工作将音乐节阵容预测形式化为未来时间戳上艺人与音乐节之间的时序链接预测任务。团队在该任务上评测了六个 TKG 预测模型，分析其能力与局限，并与零样本应用的大语言模型进行对比。这个数据集为 TKG 预测评测补充了一个基于真实应用场景的基准。
A Temporal Knowledge Graph for Music Festival Lineup Forecasting
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.
