Constructing the multimodal vector space of the accessibility graph for non-visual information access

A multimodal vector space robust to the unavailability of individual perception channels — characteristic for blind and visually impaired users — is constructed. The proposed method represents nodes in a common vector space obtained by learnable adapter projections of four channel representations (text, image, program code, and optical character recognition), their weighted fusion with the availability mask, and enrichment via attention over the graph of nearest neighbours. Training uses a joint loss with classification, consistency and contrastive components. On 2,864 arXiv-CS papers labelled by 10 Computer Science Ontology branches and under text channel unavailability, the method reaches 0.790 accuracy against 0.498 for the baseline and 0.560 for the no-enrichment version.