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arXiv · 2401.10893

Location Sensitive Embedding for Knowledge Graph Reasoning

Deepak Banerjee·Anjali Ishaan
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Abstract

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, LSEd, 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.

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Deepak Banerjee, Anjali Ishaan. 2023-12-01. Location Sensitive Embedding for Knowledge Graph Reasoning. https://arxiv.org/abs/2401.10893

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