Unlocking Advanced Graph Machine Learning Insights through Knowledge Completion on Neo4j Graph Database

2025-11-14

AI Summary

Existing graph database (GDB) and graph machine learning (GML) co-processing frameworks largely neglect knowledge completion (KC), leaving implicit semantic relations in knowledge graphs (KGs) unmodeled and resulting in fragmented graph structures that degrade GML model inference accuracy. To address this, we propose a unified GDB-GML architecture integrated with KC: it embeds scalable transitivity-aware relation modeling and decay-sensitive multi-hop knowledge propagation into Neo4j, enabling deterministic, topology-aware knowledge injection; simultaneously, it synergizes KG completion techniques with graph neural networks to support end-to-end knowledge reasoning and feature enhancement. Experiments demonstrate that our approach significantly improves graph structural completeness, boosting GML model accuracy by an average of 12.7%, while substantially enhancing modeling capability for sparse and long-tail relations.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Knowledge Acquisition

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: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Addressing knowledge completion gaps in graph database machine learning applications
• Resolving incomplete datasets that conceal valuable hidden knowledge
• Preventing wrong interpretations in graph machine learning model inputs

Innovation

• Integrates Knowledge Completion into GDB-GML applications
• Uses scalable transitive relationships with decay functions
• Propagates deterministic knowledge flows across multiple nodes