Location Sensitive Embedding for Knowledge Graph Embedding. (arXiv:2401.10893v1 [cs.IR])

Knowledge graph embedding transforms knowledge graphs into a continuous, low- dimensional space, facilitating inference and completion tasks. This field is mainly divided into 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, the 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

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