Papers by Xianggan Liu
Commonsense Subgraph for Inductive Relation Reasoning with Meta-learning (2025.coling-main)
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| Challenge: | Existing subgraph-based models focus on predicting missing relations in knowledge graphs . a new meta-learning model extracts concepts from entities to construct commonsense subgraphs based on semantic information . |
| Approach: | They propose a commonsense subgraph meta-learning model that extracts concepts from entities to construct commonsensible subgraphs. |
| Outcome: | The proposed model outperforms existing models in inductive reasoning tasks and in few-shot scenarios. |
Dynamic Graph Navigation via Triplet Chains for Structure-Aware Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is a strategy to mitigate hallucination and factual errors in large language models (LLMs). |
| Approach: | They propose a structure-aware RAG which achieves noise removal in retrieval through multi-chain graph navigation reasoning. |
| Outcome: | The proposed method achieves noise removal in retrieval through multi-chain graph navigation reasoning (Trig-Nav) compared to baseline methods, it significantly improves the model’s performance, validating the effectiveness of this approach. |
LGA: LLM-GNN Aggregation for Temporal Evolution Attribute Graph Prediction (2025.emnlp-main)
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| Challenge: | Current methods focus on 1-hop neighborhood aggregation, lacking capability to capture complex structural interactions. |
| Approach: | They propose a framework that integrates structural information into attribute embeddings through an attribute embedded loss. |
| Outcome: | The proposed framework shows significant improvements over existing methods on real-world datasets. |