Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods

תקציר מקורי באנגליתarXiv:2506.05626v3 Announce Type: replace Abstract: Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, Knowledge Graphs (KGs) are structured representations that integrate heterogeneous data sources into structured representations. However, KGs typically reduce complex n-ary relations to simple triples, thereby losing higher-order relational details. In contrast, hypergraphs naturally represent n-ary relations with hyperedges that directly connect multiple entities. Recent advances have led to many hypergraph representation learning methods; however, they often overlook entity roles in hyperedges, limiting fine-grained semantic modelling. To address these issues, Knowledge Hypergraphs (KHGs) and Hyper-relational Knowledge