Knowledge graph (KG) schema, which prescribes a high-level structure and semantics of a KG, is significantly helpful for KG completion and reasoning problems. Despite its usefulness, open-domain KGs do not practically have a unified and fixed schema. Existing approaches usually extract schema information using entity types from a KG where each entity 𝑒 can be associated with a set of types {𝑇𝑒 }, by either heuristically taking one type for each entity or exhaustively combining the types of all entities in a fact (to get entity-typed tuples, (ℎ_𝑡𝑦𝑝𝑒, 𝑟, 𝑡_𝑡𝑦𝑝𝑒) for example). However, these two approaches either overlook the role of multiple types of a single entity across different facts or introduce nonnegligible noise as not all the type combinations actually support the fact, thus failing to capture the sophisticated schema information. Against this background, we study the problem of modeling hyper-relational schema, which is formulated as mixed hyperrelational tuples ({𝑇ℎ}, 𝑟, {𝑇𝑡 }, 𝑘1, {𝑇𝑣1 }, ...) with two-fold hyperrelations: each type set {𝑇 } may contain multiple types and each schema tuple may contain multiple key-type set pairs (𝑘, {𝑇𝑣 }). To address this problem, we propose HELIOS, a hyper-relational schema model designed to subtly learn from such hyper-relational schema tuples by capturing not only the correlation between multiple types of a single entity, but also the correlation between types of different entities and relations in a schema tuple. We evaluate HELIOS on three real-world KG datasets in different schema prediction tasks. Results show that HELIOS consistently outperforms state-of-the-art hyper-relational link prediction techniques by 20.0- 29.7%, and is also much more robust than baselines in predicting types and relations across different positions in a hyper-relational schema tuple.