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PosKHG: A Position-Aware Knowledge Hypergraph Model for Link Prediction
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AI
DOI：10.1007/s41019-023-00214-x.png)
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Abstract
En 中文
Link prediction in knowledge hypergraphs is essential for various knowledge-based applications, including question answering and recommendation systems. However, many current approaches simply extend binary relation methods from knowledge graphs to n-ary relations, which does not allow for capturing entity positional and role information in n-ary tuples. To address this issue, we introduce PosKHG, a method that considers entities' positions and roles within n-ary tuples. PosKHG uses an embedding space with basis vectors to represent entities' positional and role information through a linear combination, which allows for similar representations of entities with related roles and positions. Additionally, PosKHG employs a relation matrix to capture the compatibility of both information with all associated entities and a scoring function to measure the plausibility of tuples made up of entities with specific roles and positions. PosKHG achieves full expressiveness and high prediction efficiency. In experimental results, PosKHG achieved an average improvement of 4.1% on MRR compared to other state-of-the-art knowledge hypergraph embedding methods. Our code is available at https://anonymous.4open.scien ce/r/PosKHG-C5B3/.
Keywords:
Link prediction
Knowledge hypergraph
Positional embedding
Role