Formulating and answering logical queries is a standard communication interface for
knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural …

Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem
as traditional subgraph matching methods are not capable to deal with noise and missing …

Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep
understanding of the complex relationships between entities and the underlying logic of their …

Knowledge graphs (KGs) capture knowledge in the form of head--relation--tail triples and
are a crucial component in many AI systems. There are two important reasoning tasks on …

Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot of
attention to potentially support many applications. Given that KGs are usually incomplete …

Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a challenging
problem due to massive and complicated structures in many KGs. Recently, many promising …

Answering complex queries on knowledge graphs is important but particularly challenging
because of the data incompleteness. Query embedding methods address this issue by …

Logical reasoning over Knowledge Graphs (KGs) for first-order logic (FOL) queries performs
the query inference over KGs with logical operators, including conjunction, disjunction …

Complex Query Answering (CQA) over Knowledge Graphs (KGs) is a challenging task.
Given that KGs are usually incomplete, neural models are proposed to solve CQA by …

Geometric relational embeddings map relational data as geometric objects that combine
vector information suitable for machine learning and structured/relational information for …