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 Query-Enhanced Adaptive Semantic Path
Reasoning (QASPR) framework, which simultaneously captures both the structural
and semantic information of KGs to enhance the inductive KGC task.
Specifically, the proposed QASPR employs a query-dependent masking module to
adaptively mask noisy structural information while retaining important
information closely related to the targets. Additionally, QASPR 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 QASPR achieves state-of-the-art
performance.

本文提出了 Query-Enhanced Adaptive Semantic Path Reasoning (QASPR) 框架，同时捕捉知识图谱的结构和语义信息，以增强归纳式知识图谱完成任务。QASPR 通过使用查询依赖的掩蔽模块自适应地掩蔽噪声结构信息并保留与目标密切相关的重要信息。此外，QASPR 引入了一个全局语义评分模块，评估知识图谱中推理路径上节点的个体贡献和集体影响。实验结果表明，QASPR 实现了最先进的性能。

查询增强的适应性语义路径推理用于归纳式知识图谱补全

Query-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge  Graph Completion

Knowledge Graph Completion (KGC) has garnered massive research interest
recently, and most existing methods are designed following a transductive
setting where all entities are observed during training. Despite the great
progress on the transductive KGC, these methods struggle to conduct reasoning
on emerging KGs involving unseen entities. Thus, inductive KGC, which aims to
deduce missing links among unseen entities, has become a new trend. Many
existing studies transform inductive KGC as a graph classification problem by
extracting enclosing subgraphs surrounding each candidate triple.
Unfortunately, they still face certain challenges, such as the expensive time
consumption caused by the repeat extraction of enclosing subgraphs, and the
deficiency of entity-independent feature learning. To address these issues, we
propose a global-local anchor representation (GLAR) learning method for
inductive KGC. Unlike previous methods that utilize enclosing subgraphs, we
extract a shared opening subgraph for all candidates and perform reasoning on
it, enabling the model to perform reasoning more efficiently. Moreover, we
design some transferable global and local anchors to learn rich
entity-independent features for emerging entities. Finally, a global-local
graph reasoning model is applied on the opening subgraph to rank all
candidates. Extensive experiments show that our GLAR outperforms most existing
state-of-the-art methods.

基于全局和局部锚点表示（GLAR）学习的归纳式知识图补全方法，在开放子图上进行推理，以更高效地评估候选实体，并通过全局和局部锚点学习丰富的独立特征来解决传统方法的问题。实验证明，GLAR 优于现有大多数最先进的方法。

一个子图用于全部：归纳知识图谱补全中有效推理

One Subgraph for All: Efficient Reasoning on Opening Subgraphs for  Inductive Knowledge Graph Completion

Knowledge graph completion (KGC) aims to utilize existing knowledge to deduce
and infer missing connections within knowledge graphs. Text-based approaches,
like SimKGC, have outperformed graph embedding methods, showcasing the promise
of inductive KGC. However, the efficacy of text-based methods hinges on the
quality of entity textual descriptions. In this paper, we identify the key
issue of whether large language models (LLMs) can generate effective text. To
mitigate hallucination in LLM-generated text in this paper, we introduce a
constraint-based prompt that utilizes the entity and its textual description as
contextual constraints to enhance data quality. Our Constrained-Prompt
Knowledge Graph Completion (CP-KGC) method demonstrates effective inference
under low resource computing conditions and surpasses prior results on the
WN18RR and FB15K237 datasets. This showcases the integration of LLMs in KGC
tasks and provides new directions for future research.

本研究使用文本为基础的方法提升了知识图谱补全的效果，并通过引入约束型提示来改善大型语言模型生成的文本质量，该方法在低资源计算条件下展现了有效的推理能力，并在 WN18RR 和 FB15K237 数据集上超越了之前的结果，为将大型语言模型应用于知识图谱补全任务提供了新的研究方向。