将知识补全融入图数据库机器学习，揭示隐藏关系提升模型效果
Unlocking Advanced Graph Machine Learning Insights through Knowledge Completion on Neo4j Graph Database
- 在图数据库中引入知识补全阶段，通过衰减函数建模传递关系
- 实验显示补全后图结构与数据动态发生根本性改变
- 适合关注图数据质量与分析深度的研究者使用
图机器学习（GML）结合图数据库（GDB）近年来备受关注，因其能处理复杂关联数据并应用图数据科学（GDS）技术。然而，现有GDB-GML应用在分析数据时存在关键缺陷：忽视知识图谱（KG）中的知识补全（KC），即使数据看似不完整或碎片化，实则蕴含潜在知识。这一局限可能导致模型输入错误解读。本文提出一种创新架构，将知识补全阶段集成至GDB-GML流程中，证明揭示隐藏知识可显著影响数据集的行为与指标。为此，我们引入可扩展的传递关系，即通过衰减函数建模信息在网络中跨节点传播，实现确定性知识流动。实验表明，该方法彻底重塑了图拓扑与整体数据动态，凸显新架构对生成更优模型、释放图数据分析潜力的必要性。
原文摘要 · Abstract (English)
Graph Machine Learning (GML) with Graph Databases (GDBs) has gained significant relevance in recent years, due to its ability to handle complex interconnected data and apply ML techniques using Graph Data Science (GDS). However, a critical gap exists in the current way GDB-GML applications analyze data, especially in terms of Knowledge Completion (KC) in Knowledge Graphs (KGs). In particular, current architectures ignore KC, working on datasets that appear incomplete or fragmented, despite they actually contain valuable hidden knowledge. This limitation may cause wrong interpretations when these data are used as input for GML models. This paper proposes an innovative architecture that integrates a KC phase into GDB-GML applications, demonstrating how revealing hidden knowledge can heavily impact datasets' behavior and metrics. For this purpose, we introduce scalable transitive relationships, which are links that propagate information over the network and modelled by a decay function, allowing a deterministic knowledge flows across multiple nodes. Experimental results demonstrate that our intuition radically reshapes both topology and overall dataset dynamics, underscoring the need for this new GDB-GML architecture to produce better models and unlock the full potential of graph-based data analysis.
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