Intelligent Cultural Heritage Metadata and Knowledge Graph (ICMM + CKG)

Intelligent Cultural Heritage Metadata and Knowledge Graph (ICMM + CKG) An open-source framework inspired by “The Foundation Layer of Smart Museum Digital Resource Systems” , integrating Integrated Cultural Metadata Model (ICMM) and Cultural Knowledge Graph (CKG) for semantic organization, intelligent retrieval, and cross-museum collaboration. 🌍 Vision In the era of digital transformation, traditional “storage-driven” museum databases fail to meet the requirements of semantic interoperability, intelligent access, and multimodal interaction.This project proposes a knowledge-driven system architecture that unifies metadata, knowledge graphs, and semantic reasoning. Key goals: Establish a standardized metadata model (ICMM) integrating objects, events, and relationships. Build a Cultural Knowledge Graph (CKG) to enable intelligent retrieval and reasoning. Enable multimodal annotation combining text and images. Support cross-museum interoperability and personalized cultural experiences. 🎯 Objectives and Contributions Standardization: Aligns with international cultural heritage models (CIDOC CRM, IIIF, LIDO). Semantic Intelligence: Introduces ontology-based reasoning and graph-based inference. Interoperability: Enables cross-institutional data sharing and aggregation. Scalability: Supports incremental updates and semantic enrichment of museum data. Practicality: Provides reusable ontology schemas, mapping templates, and data validation methods. 🧠 Core Components 1. Integrated Cultural Metadata Model (ICMM) Defines three core layers: Object, Event, and Relation. Establishes mappings with international metadata standards (CIDOC CRM, IIIF, LIDO). Ensures global identifiers and version tracking for cultural assets. 2. Cultural Knowledge Graph (CKG) Performs entity extraction, fusion, and ontology alignment. Supports reasoning across entities, events, and spatial-temporal contexts. Enables contextual exploration across cultural timelines and exhibition narratives. 3. Multimodal Annotation & Semantic Retrieval Integrates text and image-based metadata for richer understanding. Supports automatic tag suggestion and human-in-the-loop verification. Provides semantic search, path-based exploration, and visualization tools. 4. Interoperability & API Layer Data exchange via JSON-LD, RDF/Turtle, and linked data formats. Exposes REST and GraphQL APIs for retrieval, reasoning, and export. Offers compatibility with graph databases (Neo4j, Jena, Blazegraph, etc.). 🧩 System Architecture Overview Data Acquisition and NormalizationRaw data from museum collections, archives, and exhibitions are standardized via ICMM. Ontology and Reasoning EngineApplies inference rules (RDFS/OWL) for class hierarchies, entity alignment, and implicit relations. Knowledge Graph ConstructionFuses structured and unstructured data into the CKG with semantic enrichment. Semantic Services and VisualizationProvides intelligent search, recommendation, and graph-based visualization interfaces. 🔍 Evaluation Metrics To ensure reliability and interoperability, the system can be evaluated on: Semantic Retrieval: NDCG@K, MRR, Recall@K Entity Alignment: Precision / Recall / F1 Multimodal Annotation: Top-K accuracy, average confidence, correction cost Graph Consistency: SHACL validation coverage and error rate These benchmarks can be adapted for cross-institutional evaluation and data integration tasks. 🧭 Roadmap v0.1: ICMM base ontology and SHACL validation rules v0.2: Metadata mapping templates (CIDOC/IIIF/LIDO) v0.3: Reasoning engine and data validation pipeline v0.4: Prototype multimodal annotation and semantic search module v1.0: Public release with cross-museum demonstration dataset 🤝 Contribution Guidelines Open issues for new features, ontology updates, or bug reports. Ensure all changes pass ontology validation and consistency checks. Document every schema or mapping change clearly. Follow conventional commit messages and pull request templates. 📜 License Licensed under MIT. 🙏 Acknowledgements This repository is inspired by ongoing research in digital cultural heritage, particularly the work presented in “The Foundation Layer of Smart Museum Digital Resource Systems”.Gratitude is extended to the global open-source and heritage informatics community for continuous contributions to metadata standards and cultural knowledge modeling.