Logic-Aware Knowledge Graph Reasoning for Structural Sparsity under Large Language Model Supervision

Yudai Pan, Jiajie Hong, Tianzhe Zhao, Lingyun Song, Jun Liu, Xuequn Shang

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Knowledge Graph (KG) reasoning aims to predict missing entities in incomplete triples, which requires adequate structural information to derive accurate embeddings. However, KGs in the real world are not as dense as the idealized benchmarks, where sparse graph structures restrict the comprehensive structural information for superior performance. Although the logical semantics in KGs shows its potential in alleviating the impact of structural sparsity, there still exist some challenges. The deficient supervision and the semantic gap of logic make it difficult to introduce logical semantics in sparse KG reasoning. To this end, we propose a novel KG reasoning approach LoLLM injecting logic with the supervised information supplied by the Large Language Model (LLM), which is proved to be effective in evaluating and scoring. Firstly, LoLLM derives structural embeddings employing a graph convolutional network (GCN) with relation-aware and triple-aware attention. LoLLM secondly constructs reasoning paths instantiated from the first-order logic rules extracted from sparse KGs, and injects the logical semantics by a designed LLM-enhanced tuning strategy. We propose a textual loss (TL) and a logical loss (LL) in the optimization and obtain logical tuning embeddings of KG in this process. Finally, LoLLM fuses structural embeddings from the GCN and logical tuning embeddings from the LLM-enhanced tuning for scoring and incomplete triple prediction. Extensive experiments on two sparse KGs and a benchmark show that LoLLM outperforms state-of-the-art structure-based and Language Model (LM)-augmented baselines. Moreover, the logic rules with corresponding confidences provide explicit explanations as an interpretable paradigm.

源语言英语
主期刊名WWW 2025 - Proceedings of the ACM Web Conference
出版商Association for Computing Machinery, Inc
4531-4542
页数12
ISBN(电子版)9798400712746
DOI
出版状态已出版 - 28 4月 2025
活动34th ACM Web Conference, WWW 2025 - Sydney, 澳大利亚
期限: 28 4月 20252 5月 2025

出版系列

姓名WWW 2025 - Proceedings of the ACM Web Conference

会议

会议34th ACM Web Conference, WWW 2025
国家/地区澳大利亚
Sydney
时期28/04/252/05/25

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