用于音乐节阵容预测的时间知识图谱
A Temporal Knowledge Graph for Music Festival Lineup Forecasting
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- University of Mannheim（曼海姆大学）
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中文总结 AI 辅助
提出一个覆盖55年380个音乐节、含9万多个演出四元组的时间知识图谱，将阵容预测形式化为时间链接预测，评估六种TKG模型并与零样本LLM对比，为TKG预测提供真实应用基准。
中文摘要 AI 辅助
音乐节阵容源于艺术家、流派、发行、厂牌及过往演出之间的复杂关系，这使得预测未来阵容天然适合时间知识图谱（TKG）预测任务。在本工作中，我们提出了一个覆盖55年间380个音乐节的TKG，包含超过9万个音乐节演出四元组以及音乐节、艺术家巡演和艺术家元数据信息，并将其作为TKG预测评估的资源发布。我们将音乐节阵容预测形式化为未来时间戳下艺术家与音乐节之间的时间链接预测。我们在此任务上评估了六种TKG预测模型，分析了它们的能力与局限，并将其与零样本应用的大型语言模型进行了比较。我们的资源通过将评估锚定在具体、真实的应用领域，补充了现有TKG基准。
英文摘要
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.
