Abstract
This paper presents a multimodal approach to the artificial intelligence of the digital presentation and preservation of cultural and scientific heritage. Integrating the multiple modalities of indexing, automated metadata enrichment and semantic linking, the framework provides better archival discoverability. The main contribution is a non-destructive architecture that utilizes a shared semantic space to fuse text, visual, and audio modalities while employing a Semantic Anchor strategy to prevent the erasure of "thin" legacy metadata. Experimental findings demonstrate a Mean Precision @ 5 of 0.92 and a 100% Data Retention rate, proving that the framework successfully reclaims "dark data" while maintaining high retrieval consistency across heterogeneous multimedia collections.