发表机构
Bandung Fe Institute（万隆Fe研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对文化遗产数字盘点中人工贡献的覆盖、完整与深度障碍，提出五阶段AI采集漏斗框架，在确定性编排下提升机器自主性，同时保留人类关键角色并保证机器不覆盖人类贡献。
AI 中文摘要
印度尼西亚数字文化图书馆（Perpustakaan Digital Budaya Indonesia, PDBI；网址：http://pdb-i.lipi.go.id）是一个参与式平台，自2007年以来通过公众贡献收集了数万条关于努桑塔拉文化遗产的条目。人工贡献面临三个结构性障碍：覆盖度（知识分散在不同语言和站点中）、完整性（开放来源将真实文档与噪声混合）以及深度（主题被记录但其数据仍然浅薄）。本文提出了一种方法论框架，用于从开放网络中自主、基于人工智能地采集文化知识，旨在扩展语料库覆盖度的同时加深每个条目的数据深度。该方法论组织为一个五阶段经济漏斗：聚焦爬取、多语言提取与规范化、带分块的向量编码、智能体决策以及幂等发布，遵循“确定性编排、智能体决策”的原则。每个阶段都被形式化：漏斗经济学与最优过滤器排序；爬行前沿动态作为亚临界分支过程，解释了反复重新播种的必要性；通过包含度度量实现的事实级新颖性；贝叶斯多源证据融合，对神圣类别设置更高的发布阈值；通过幂等更新和事务性发件箱实现恰好一次效果；带预留协议的滑动窗口推理预算；统计质量审计；以及将种子选择作为子模覆盖最大化。该框架保留了四个高价值的人类角色：方向策展人、升级审批人、质量审计员和意义守护者，同时机器自主性分阶段提升。文章讨论了伦理、法律和文化敏感性影响，包括机器永不覆盖人类贡献的架构保证。
英文摘要
The Indonesian Digital Library of Culture (Perpustakaan Digital Budaya Indonesia, PDBI; budaya-indonesia.org) is a participatory platform that has collected tens of thousands of entries on Nusantara cultural heritage through public contribution since 2007. Manual contribution faces three structural barriers: coverage (knowledge is scattered across languages and sites), integrity (open sources mix authentic documentation with noise), and completeness (subjects are recorded but their data remain shallow). This paper presents a methodological framework for autonomous, AI-based harvesting of cultural knowledge from the open web, designed to expand corpus coverage while intensifying per-entry data depth. The methodology is organised as a five-stage economic funnel: focused crawling, multilingual extraction and canonicalisation, vector encoding with blocking, agentic decision-making, and idempotent publication, under the principle of deterministic orchestration, agentic decisions. Each stage is formalised: funnel economics and optimal filter ordering; crawl-frontier dynamics as a subcritical branching process that explains the necessity of recurrent re-seeding; fact-level novelty via a containment measure; Bayesian multi-source evidence fusion with elevated publication thresholds for sacred categories; exactly-once effects via idempotent upserts and the transactional outbox; sliding-window inference budgeting with a reservation protocol; statistical quality auditing; and seed selection as submodular coverage maximisation. The framework retains four high-value human roles: curator of direction, escalation approver, quality auditor, and guardian of meaning, while machine autonomy is raised in stages. Ethical, legal, and cultural-sensitivity implications are discussed, including the architectural guarantee that the machine never overwrites human contributions.
Comments11 pages, 3 figures
