Papers with knowledge-based inference

2 papers
LinkNBed: Multi-Graph Representation Learning with Entity Linkage (P18-1)

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Challenge: Knowledge graphs have emerged as an important model for studying complex multi-relational data.
Approach: They propose a deep relational learning framework that learns entity and relationship representations across multiple graphs.
Outcome: The proposed framework improves on the state-of-the-art relational learning approaches and identifies entity linkage across graphs.
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners (2025.emnlp-main)

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Challenge: Existing knowledge editing methods fail to generalize updates to multi-hop reasoning tasks . Existing methods only edit single or a few model layers, inadequately integrate updated knowledge into reasoning pathways.
Approach: They propose a circuit-aware method that enhances the effective integration of updated knowledge in large language models by leveraging curated data samples guided by their analysis.
Outcome: The proposed method improves accuracy and accuracy of 20% on the MQuAKE dataset while requiring less memory.

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