Multi-hop logical reasoning is an established problem in the field of
representation learning on knowledge graphs (KGs). It subsumes both one-hop
link prediction as well as other more complex types of logical queries.
Existing algorithms operate only on classical, triple-based graphs, whereas
modern KGs often employ a hyper-relational modeling paradigm. In this paradigm,
typed edges may have several key-value pairs known as qualifiers that provide
fine-grained context for facts. In queries, this context modifies the meaning
of relations, and usually reduces the answer set. Hyper-relational queries are
often observed in real-world KG applications, and existing approaches for
approximate query answering cannot make use of qualifier pairs. In this work,
we bridge this gap and extend the multi-hop reasoning problem to
hyper-relational KGs allowing to tackle this new type of complex queries.
Building upon recent advancements in Graph Neural Networks and query embedding
techniques, we study how to embed and answer hyper-relational conjunctive
queries. Besides that, we propose a method to answer such queries and
demonstrate in our experiments that qualifiers improve query answering on a
diverse set of query patterns.

本文介绍了如何利用 Graph Neural Networks 来嵌入和回答超关系连接查询，以处理新型复杂查询，同时提出了一种提高查询效率的方法，并在实验证明 Qualifier 可以在多种查询模式下提高查询效果。