Indonesian AI system expands and deepens cultural heritage records online
Artificial Intelligence-Assisted Digital Inventory of Cultural Heritage & Traditional Knowledge: Case for Indonesian Open Digital Library of Culture
Artificial IntelligenceDigital LibrariesHuman-Computer InteractionInformation Retrieval
Summary
Collecting and organizing cultural information from many scattered sources is hard, and people do it slowly by hand. The authors created a method using AI to automatically find, verify, and add cultural knowledge about Indonesia from the web, making entries both broader and more detailed. They keep humans involved to oversee sensitive content and ensure quality, while the AI handles most of the data gathering. This approach aims at preserving cultural heritage in a trustworthy and scalable digital library.
What this means in practice
- •For digital library managers: Automatically expand and enrich cultural heritage databases by harvesting verified knowledge from multiple web sources while keeping human curators in control.
- •For cultural heritage organizations: Maintain up-to-date, comprehensive digital archives that combine human oversight with AI-driven content aggregation to document diverse cultural knowledge.
Authors
Hokky Situngkir
Abstract
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.