Cumulative Path-Level Semantic Reasoning for Inductive Knowledge Graph Completion

• URL: http://arxiv.org/abs/2601.05629v1
• Date: Fri, 09 Jan 2026 08:34:05 GMT
• Title: Cumulative Path-Level Semantic Reasoning for Inductive Knowledge Graph Completion
• Authors: Jiapu Wang, Xinghe Cheng, Zezheng Wu, Ruiqi Ma, Rui Wang, Zhichao Yan, Haoran Luo, Yuhao Jiang, Kai Sun,
• Abstract summary: This paper proposes the Cumulative Path-Level Semantic Reasoning for inductive knowledge graph completion (CPSR) framework.<n>CPSR simultaneously captures both the structural and semantic information of KGs to enhance the inductive KGC task.
• Score: 9.623163073915741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SocraticKG: Knowledge Graph Construction via QA-Driven Fact Extraction [4.867319754310031]
  We propose an automated KG construction method that introduces question-answer pairs as a structured intermediate representation.<n>SocraticKG captures contextual dependencies and implicit relational links typically lost in direct KG extraction pipelines.
  arXiv  Detail & Related papers  (2026-01-15T02:26:51Z)
• StruProKGR: A Structural and Probabilistic Framework for Sparse Knowledge Graph Reasoning [68.58655814341996]
  Sparse Knowledge Graphs (KGs) are commonly encountered in real-world applications, where knowledge is often incomplete or limited.<n>We propose a Structural and Probabilistic framework named StruProKGR, tailored for efficient and interpretable reasoning on sparse KGs.
  arXiv  Detail & Related papers  (2025-12-14T09:36:58Z)
• KGRAG-Ex: Explainable Retrieval-Augmented Generation with Knowledge Graph-based Perturbations [2.287415292857565]
  Knowledge graphs (KGs) offer a solution by introducing structured, semantically rich representations of entities and their relationships.<n>We present KGRAG-Ex, a RAG system that improves both factual grounding and explainability by leveraging a domain-specific KG.<n>Given a user query, KGRAG-Ex identifies relevant entities and semantic paths in the graph, which are then transformed into pseudo-paragraphs.
  arXiv  Detail & Related papers  (2025-07-11T09:35:13Z)
• LKD-KGC: Domain-Specific KG Construction via LLM-driven Knowledge Dependency Parsing [9.502380540548497]
  Knowledge Graphs (KGs) structure real-world entities and their relationships into triples, enhancing machine reasoning for various tasks.<n>Recent approaches for knowledge graph construction based on large language models (LLMs) have proven efficient.<n>We propose LKD-KGC, a novel framework for unsupervised domain-specific KG construction.
  arXiv  Detail & Related papers  (2025-05-30T03:10:23Z)
• Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models [51.47994645529258]
  We propose Question-Aware Knowledge Graph Prompting (QAP), which incorporates question embeddings into GNN aggregation to dynamically assess KG relevance.<n> Experimental results demonstrate that QAP outperforms state-of-the-art methods across multiple datasets, highlighting its effectiveness.
  arXiv  Detail & Related papers  (2025-03-30T17:09:11Z)
• Grounding LLM Reasoning with Knowledge Graphs [4.279373869671241]
  We propose integrating reasoning strategies with Knowledge Graphs to anchor every step or "thought" of the reasoning chains in KG data.<n>We evaluate both agentic and automated search methods across several reasoning strategies, including Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT)<n>Our experiments demonstrate that this approach consistently outperforms baseline models.
  arXiv  Detail & Related papers  (2025-02-18T19:20:46Z)
• Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting [50.181824673039436]
  We propose a Graph Structure Self-Contrasting (GSSC) framework that learns graph structural information without message passing. The proposed framework is based purely on Multi-Layer Perceptrons (MLPs), where the structural information is only implicitly incorporated as prior knowledge. It first applies structural sparsification to remove potentially uninformative or noisy edges in the neighborhood, and then performs structural self-contrasting in the sparsified neighborhood to learn robust node representations.
  arXiv  Detail & Related papers  (2024-09-09T12:56:02Z)
• Query-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completion [45.9995456784049]
  This paper proposes the Query-Enhanced Adaptive Semantic Path Reasoning (QASPR) framework. QASPR captures both the structural and semantic information of KGs to enhance the inductive KGC task. experimental results demonstrate that QASPR achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-06-04T11:02:15Z)
• Contextualization Distillation from Large Language Model for Knowledge Graph Completion [51.126166442122546]
  We introduce the Contextualization Distillation strategy, a plug-in-and-play approach compatible with both discriminative and generative KGC frameworks. Our method begins by instructing large language models to transform compact, structural triplets into context-rich segments. Comprehensive evaluations across diverse datasets and KGC techniques highlight the efficacy and adaptability of our approach.
  arXiv  Detail & Related papers  (2024-01-28T08:56:49Z)
• Anchoring Path for Inductive Relation Prediction in Knowledge Graphs [69.81600732388182]
  APST takes both APs and CPs as the inputs of a unified Sentence Transformer architecture. We evaluate APST on three public datasets and achieve state-of-the-art (SOTA) performance in 30 of 36 transductive, inductive, and few-shot experimental settings.
  arXiv  Detail & Related papers  (2023-12-21T06:02:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.