Researchers propose a new data representation called labeled incidence structures (LIS) for handling text, knowledge graphs, and hypergraphs in transformer models. LIS encodes each endpoint as a combination of content, role, and relation instance, allowing a single transformer to process these data types natively without flattening. This representation enables role- and relation-aware attention and can improve model performance in certain scenarios.