Papers with HKG

2 papers
HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level (2023.acl-long)

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Challenge: Existing research on HKGs rarely models the graphical and sequential structure of HKG, limiting their representation.
Approach: They propose a Hierarchical Attention model for HKG Embedding that includes global-level and local-level attention to model the graphical structure of HKGs.
Outcome: The proposed model achieves state-of-the-art performance on HKG standard datasets and addresses the issue of HKG multi-position prediction for the first time.
Dynamic Heterogeneous-Graph Reasoning with Language Models and Knowledge Representation Learning for Commonsense Question Answering (2023.acl-long)

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Challenge: Existing methods for QA use knowledge graphs, but they ignore subgraph optimization and subgraph deepening.
Approach: They propose a dynamic heterogeneous-graph reasoning method with LMs and knowledge representation learning that optimizes the structure and knowledge representing of the HKG using a two-stage pruning strategy and knowledge-representation learning.
Outcome: The proposed method improves on existing methods at CommonsenseQA and OpenBookQA.

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