ASENet & CRP-Strategy: Context-Aware Semantic Modeling for Smart Museum Displays

ASENet & CRP-Strategy: Context-Aware Semantic Modeling for Smart Museum Displays A unified framework for intelligent management and adaptive sequencing of museum digital resources using Artifact-Aware Semantic Embedding Network (ASENet) and the Contextual Relevance Projection Strategy (CRP-Strategy). Overview This repository implements ASENet and CRP-Strategy, two complementary modules for semantic representation, contextual personalization, and adaptive exhibit sequencing in museum informatics and cultural heritage display systems. The project integrates multimodal learning (visual, textual, and contextual data) with symbolic and intent-based reasoning to deliver adaptive, interpretable, and narrative-consistent exhibition experiences. Key Features ASENet (Multimodal Semantic Encoder): Jointly embeds image, text, and context data in a shared latent space. Utilizes ontology-guided alignment to preserve curatorial semantics. Enables cross-modal retrieval, classification, and clustering of museum assets. CRP-Strategy (Contextual Relevance Projection): Dynamically adapts exhibit sequencing using context such as time, location, and visitor behavior. Models latent visitor intent distributions for personalization. Performs constrained sequence optimization with utility and diversity balancing. Performance: Validated on four cultural datasets with state-of-the-art results in retrieval, personalization, and narrative cohesion. Demonstrated through a prototype deployment at Tianjin Museum, improving engagement and adaptive storytelling. Dataset Preparation This repository is designed to interoperate with multiple open-access museum datasets for experimentation and benchmarking.Below are four recommended datasets corresponding to the paper’s evaluation section. Dataset Description Link Museum Digital Asset Dataset High-resolution digitized museum collections from the Smithsonian Institution. Includes images and metadata for multimodal learning. 🔗 Smithsonian Open Access Smart Exhibit Interaction Dataset Interaction and engagement logs from open-access collections at The Metropolitan Museum of Art, supporting visitor-behavior modeling. 🔗 The Met Open Access Cultural Artifact Metadata Dataset A structured dataset describing visitor behavior patterns, artifact metadata, and contextual relationships for semantic reasoning. 🔗 Visitor Behavior Patterns Dataset (Hugging Face) Visitor Engagement Tracking Dataset Video and sensor-based data for visitor motion and engagement analysis in exhibition spaces. 🔗 Museum Visitors Dataset (MICC Florence) Usage Note:Each dataset may have its own licensing terms (CC0, CC BY, or institutional policies).Please review individual dataset documentation and cite the original providers when publishing derived works. Quick Start Requirements Python ≥ 3.10 No external deep learning dependencies required for demo mode Optional: PyTorch (for custom extensions) Architecture Overview ASENet Encoder Processes image (V), text (T), and context (C) through modular encoders and fuses them to form unified semantic embeddings. CRP-Strategy Uses visitor and spatial context for: Intent inference Contextual scoring Sequence optimization with utility maximization and diversity constraints. Evaluation Summary Model Personalization Accuracy User Satisfaction Latency ASENet Only 72.3% 3.9 / 5 240 ms ASENet + CRP-Strategy 88.1% 4.6 / 5 310 ms ASENet + CRP significantly improves adaptive recommendations and narrative continuity in simulated and real deployments. Applications Cultural Heritage Informatics: Intelligent indexing and retrieval of digital artifacts. Smart Museum Exhibitions: Personalized storytelling and adaptive sequencing. Education & Public Engagement: Interactive installations and virtual gallery systems. Data-Driven Curation: Ontology-aligned analysis and visualization. Future Work Real-time sensory integration (gaze, gesture, emotion). Federated cross-museum learning for privacy-aware collaboration. Multilingual narrative generation from metadata. Integration with IIIF, CIDOC CRM, and JSON-LD for interoperability. License This project is released under the MIT License. Acknowledgements Thanks to: Smithsonian Institution Open Access Initiative The Metropolitan Museum of Art Open Access Project MICC Florence and the Visitor Behavior Patterns dataset contributors Original authors of the ASENet + CRP-Strategy paper for providing the conceptual foundation.ASENet与CRP-Strategy：面向智慧博物馆展陈的上下文感知语义建模 本项目基于文物感知语义嵌入网络（Artifact-Aware Semantic Embedding Network，ASENet）与上下文相关性投影策略（Contextual Relevance Projection Strategy，CRP-Strategy），构建了一套用于博物馆数字资源智能管理与自适应排序的统一框架。 ## 项目概览 本代码仓库实现了ASENet与CRP-Strategy两个互补模块，可用于博物馆信息学与文化遗产展陈系统中的语义表征、上下文个性化定制与自适应展品排序。 本项目将多模态学习（涵盖视觉、文本与上下文数据）与符号化、基于意图的推理相结合，可提供自适应、可解释且叙事一致的观展体验。 ## 核心特性 ### ASENet（多模态语义编码器） 1. 将图像、文本与上下文数据联合嵌入至统一隐空间中。 2. 采用本体引导的对齐方式保留策展语义。 3. 支持博物馆馆藏资源的跨模态检索、分类与聚类。 ### CRP-Strategy（上下文相关性投影策略） 1. 基于时间、位置与访客行为等上下文信息动态调整展品排序。 2. 对隐式访客意图分布进行建模以实现个性化定制。 3. 在兼顾效用与多样性的约束下执行序列优化。 ## 性能验证 本方法在4个文化类数据集上完成验证，在检索、个性化定制与叙事连贯性任务上均达到当前最优性能。 该方案已在天津博物馆完成原型部署验证，有效提升了观展参与度与自适应叙事效果。 ## 数据集准备 本代码仓库适配多个开源博物馆数据集以支持实验与基准测试，下文列出了对应论文评估章节的4个推荐数据集： | 数据集 | 数据集描述 | 链接 | | ---- | ---- | ---- | | 博物馆数字资产数据集 | 史密森学会（Smithsonian Institution）提供的高分辨率数字化博物馆馆藏数据集，包含用于多模态学习的图像与元数据 | 🔗 史密森学会开源平台 | | 智慧展陈交互数据集 | 大都会艺术博物馆（The Metropolitan Museum of Art）开源馆藏的交互与参与日志数据集，可用于访客行为建模 | 🔗 大都会艺术博物馆开源平台 | | 文化文物元数据集 | 用于语义推理的结构化数据集，涵盖访客行为模式、文物元数据与上下文关联关系 | 🔗 访客行为模式数据集（Hugging Face平台） | | 访客参与度追踪数据集 | 基于视频与传感器数据的展览空间访客运动与参与度分析数据集 | 🔗 博物馆访客数据集（佛罗伦萨MICC实验室） | ## 使用须知 各数据集可能拥有独立的授权协议（如CC0、CC BY或机构政策），请在发布衍生成果时查阅对应数据集的文档并引用原始提供方。 ## 快速上手 ### 环境依赖 - Python ≥ 3.10 - 演示模式无需额外深度学习依赖 - 可选：PyTorch（用于自定义扩展） ## 架构概览 ### ASENet编码器 通过模块化编码器分别处理图像（V）、文本（T）与上下文（C）数据，并将其融合为统一语义嵌入。 ### CRP-Strategy策略 基于访客与空间上下文信息实现以下功能： 1. 意图推理 2. 上下文评分 3. 兼顾效用最大化与多样性约束的序列优化 ## 评估总结 | 模型 | 个性化准确率 | 用户满意度 | 延迟 | | ---- | ---- | ---- | ---- | | 仅使用ASENet | 72.3% | 3.9/5 | 240 ms | | ASENet+CRP-Strategy | 88.1% | 4.6/5 | 310 ms | ASENet结合CRP策略可显著提升模拟与实际部署场景下的自适应推荐效果与叙事连贯性。 ## 应用场景 1. 文化遗产信息学：数字文物的智能标引与检索。 2. 智慧博物馆展陈：个性化叙事与自适应排序。 3. 教育与公众参与：交互式装置与虚拟展厅系统。 4. 数据驱动的策展：本体对齐的分析与可视化。 ## 未来工作 1. 实时感官信息融合（注视、手势、情感识别）。 2. 支持隐私保护协作的跨博物馆联邦学习。 3. 基于元数据的多语言叙事生成。 4. 集成IIIF、CIDOC CRM与JSON-LD标准以提升互操作性。 ## 开源许可 本项目采用MIT许可证开源。 ## 致谢 致谢：史密森学会开源倡议项目、大都会艺术博物馆开源项目、佛罗伦萨MICC实验室与访客行为模式数据集的贡献者，以及ASENet+CRP-Strategy论文的原作者为研究提供了概念基础。