DOI: 10.5281/zenodo.17440541
title: ASENet & CRP-Strategy: Context-Aware Semantic Modeling for Smart Museum Displays

description: 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.