Cumulative Path-Level Semantic Reasoning for Inductive Knowledge Graph Completion

2026-01-09

arXiv.org

AI Summary

This work addresses the limitations of existing inductive knowledge graph completion methods, which are susceptible to structural noise and struggle to model long-range reasoning dependencies. To overcome these challenges, the authors propose the CPSR framework, which innovatively integrates a query-aware structural masking mechanism with a path-level semantic accumulation scoring strategy. The former dynamically filters out edges irrelevant to the current query, thereby mitigating noise, while the latter employs a global semantic scoring module to evaluate both individual and collective contributions of nodes along reasoning paths, effectively capturing long-range semantic dependencies. Extensive experiments on multiple benchmark datasets demonstrate that CPSR significantly outperforms state-of-the-art approaches, achieving superior performance in inductive knowledge graph completion.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Conventional Knowledge Graph Completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to perform effectively in scenarios involving emerging entities. Inductive KGC methods can handle the emerging entities and relations in KGs, offering greater dynamic adaptability. While existing inductive KGC methods have achieved some success, they also face challenges, such as susceptibility to noisy structural information during reasoning and difficulty in capturing long-range dependencies in reasoning paths. To address these challenges, this paper proposes the Cumulative Path-Level Semantic Reasoning for inductive knowledge graph completion (CPSR) framework, which simultaneously captures both the structural and semantic information of KGs to enhance the inductive KGC task. Specifically, the proposed CPSR employs a query-dependent masking module to adaptively mask noisy structural information while retaining important information closely related to the targets. Additionally, CPSR introduces a global semantic scoring module that evaluates both the individual contributions and the collective impact of nodes along the reasoning path within KGs. The experimental results demonstrate that CPSR achieves state-of-the-art performance.

Problem

• inductive knowledge graph completion
• noisy structural information
• long-range dependencies
• reasoning paths
• emerging entities

Innovation

• inductive knowledge graph completion
• path-level reasoning
• semantic scoring
• query-dependent masking
• noise-robust reasoning