  Embedding methods transform the knowledge graph into a continuous,
low-dimensional space, facilitating inference and completion tasks. Existing
methods are mainly divided into two types: translational distance models and
semantic matching models. A key challenge in translational distance models is
their inability to effectively differentiate between 'head' and 'tail' entities
in graphs. To address this problem, a novel location-sensitive embedding (LSE)
method has been developed. LSE innovatively modifies the head entity using
relation-specific mappings, conceptualizing relations as linear transformations
rather than mere translations. The theoretical foundations of LSE, including
its representational capabilities and its connections to existing models, have
been thoroughly examined. A more streamlined variant, LSE-d, which employs a
diagonal matrix for transformations to enhance practical efficiency, is also
proposed. Experiments conducted on four large-scale KG datasets for link
prediction show that LSEd either outperforms or is competitive with
state-of-the-art related works.
