Public broadcasts are at the center of civic discourse: Traditional television talk shows, alongside emerging podcast and web video formats, capture and guide the attention of our societies, shaping how citizens encounter politics, science, and societal issues. Yet, systematic or even simple analyses of these formats face similar challenges: guest and content metadata are scarce, fleeting, fragmented, and not standardized. Research conducted and questions answered are based on extensive, laborious, yet isolated data-curation efforts that capture only a fraction of the relevant landscape. This work seeks to address this issue using a scaling-oriented framework for FAIR data curation in public broadcasting. Evaluated on 15 broadcasting programs, the pipeline aggregates ZDF Archive PDFs, fernsehserien.de, and Wikidata into a unified knowledge graph. Of the 31,817 candidate guest mentions from these three sources, 17,729 could be automatically disambiguated, further 5,958 via 64 hours of manual reconciling using OpenRefine. Results are published at speakermining.wikibase.cloud and linked to Wikidata, enabling SPARQL-based question answering based on gender, age, occupation, or institutional affiliation across 8,436 canonical persons with 23,527 appearances in 6,469 aligned episodes. Our iterative experience reveals that correctly disambiguating and deduplicating speaker data from heterogeneous sources demands dedicated effort on sustainable infrastructure. For scalable and reliable question answering on public broadcasts to be accessible to everyone, we recommend fostering the potential of linked open data: Advancing alignment and utilization approaches like this work, particularly towards crowdsourced development and curation, but also more FAIR data interfaces from public broadcast service providers.

翻译：公共广播处于公民话语的核心：传统电视谈话节目，以及新兴的播客和网络视频形式，捕捉并引导着社会的注意力，塑造公民如何理解政治、科学和社会问题。然而，对这些格式的系统性甚至简单分析都面临相似的挑战：嘉宾和内容元数据稀缺、转瞬即逝、碎片化且不标准化。所进行的研究和回答的问题都基于广泛、费力且孤立的数据整理工作，这些工作仅捕获了相关领域的一小部分。本研究旨在通过面向公共广播中FAIR数据整理的规模化框架来解决这一问题。该流水线在15个广播节目上进行评估，将ZDF存档PDF、fernsehserien.de和Wikidata整合到一个统一的知识图谱中。在这三个来源的31,817条候选嘉宾提及中，有17,729条可以自动消歧，另有5,958条通过使用OpenRefine进行64小时的手动协调完成。结果发布在speakermining.wikibase.cloud上，并与Wikidata关联，从而能够基于性别、年龄、职业或机构隶属关系进行SPARQL问答，涵盖8,436个规范人物，涉及6,469个对齐剧集中的23,527次出场。我们的迭代经验表明，正确消除异构数据源中的说话人数据歧义并去重，需要在可持续基础设施上投入专门精力。为了使关于公共广播的可扩展且可靠的问答对所有人可用，我们建议发掘关联开放数据的潜力：推进如本研究中的对齐和利用方法，特别是向众包开发和整理方向发展，同时也鼓励公共广播服务提供商提供更加FAIR的数据接口。