Location Sensitive Embedding for Knowledge Graph Reasoning

• URL: http://arxiv.org/abs/2401.10893v3
• Date: Tue, 30 Jan 2024 03:14:11 GMT
• Title: Location Sensitive Embedding for Knowledge Graph Reasoning
• Authors: Deepak Banerjee, Anjali Ishaan
• Abstract summary: 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. Experiments conducted on four large-scale KG datasets for link prediction show LSEd either outperforms or is competitive with state-of-the-art related works.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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, 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.

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