An AI Workflow Combining Bidirectional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNNs) for Knowledge Retrieval in Digital Enterprises

Summary

This protocol presents a reproducible AI-driven workflow that fine-tunes BERT for entity and relation extraction, employs graph neural networks for ontology alignment, constructs enterprise knowledge graphs from unstructured data, and systematically evaluates semantic retrieval performance and decision-support efficiency.

Abstract

Large volumes of unstructured organizational data can make it difficult for enterprise knowledge management (KM) systems to extract correct and contextually relevant information, which can lead to inefficient knowledge sharing and delayed decision-making. This study suggests a unified artificial intelligence-driven framework to overcome this limitation. It combines Graph Neural Networks (GNNs) for ontology alignment and semantic reasoning with refined Bidirectional Encoder Representations from Transformers (BERT) for domain-specific entity and relation extraction. Systematic data collection, preprocessing enterprise text corpora, fine-tuning BERT to identify entities and relationships, converting extracted triples into structured knowledge graphs, and GNN-based ontology alignment to guarantee semantic consistency across heterogeneous knowledge sources, comprise the methodological pipeline. To evaluate system efficacy in real-world enterprise scenarios, the framework also integrates task-oriented assessment measures, such as retrieval precision, ontology alignment correctness, and decision latency. When compared to baseline methods, experimental validation across two industry applications shows a 35% decrease in decision-making latency and a 21% gain in knowledge retrieval precision.

Furthermore, user feedback indicates that the KM interface has boosted user satisfaction through its semantic search and contextual tagging features. The suggested architecture facilitates reproducible knowledge graph building from unstructured enterprise data by methodically fusing graph-based reasoning and alignment with deep learning-based information extraction. The findings demonstrate that both strategic and operational KM outcomes improved when organized knowledge representations are aligned with organizational procedures. All things considered, the suggested method increases retrieval accuracy, speeds up decision workflow reaction times, and offers a workable and scalable option for enterprise-level KM systems.

Introduction

Effective KM can be difficult to adopt in digital transformation programs due to disjointed data repositories, diverse organizational platforms, and fragmented knowledge spread across unstructured documents. A reproducible, technically implementable framework that methodically extracts, structures, aligns, and operationalizes enterprise knowledge has not been proposed by much research, despite earlier studies looking at AI adoption and digital transformation from organizational and sectoral perspectives^1,2,3. Current methods focus mostly on managerial or strategic consequence....

Protocol

Ethical statement
This study was reviewed and approved by the Institutional Review Board (IRB) of The National University of Malaysia (UKM) prior to data collection (Approval ID: UKM/FEP/2025/AI-047; Approval Date: 12 March 2025). The approved protocol covered the administration of structured surveys and semi-structured interviews involving human participants. All participants were informed about the purpose of the study, the voluntary nature of their participation, and their right to withdraw at any time without consequence, and written informed consent was obtained before their inclusion. Participant anonymity and confidentiality were strictly m....

Results

Data pre-processing and BERT fine-tuning
The proposed device integrates a best tuned BERT version for unstructured understanding extraction and a Graph Neural Network (GNN) for ontology alignment and reasoning inside an understanding graph framework. The experimental setup worried comparing the overall performance of the BERT aspect on NER and RE tasks, while the GNN factor turned into examined on link prediction and node class over the built understanding graph.

The F1-sc.......

Discussion

This study presents a unified enterprise KM framework that integrates contextual semantic extraction using BERT with graph-based relational reasoning and ontology alignment through GNNs. In order to enable entity linking, cross-document reasoning, and coherent knowledge representation across disparate business data sources, the main contribution is the integration of deep contextual language modeling with structured, ontology-aware inference within a single pipeline^3,.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
BERT-Base (Uncased) Pretrained Model	Google AI	N/A	Transformer-based pretrained language model (bert-base-uncased variant)
Deep Graph Library (DGL)	AWS Labs	RRID: SCR_017054	Version 2.1 used for graph neural network modeling
Matplotlib Visualization Library	PyData Community	RRID: SCR_008624	Used for performance plots and visual analytics
NetworkX Graph Library	PyPI Community	RRID: SCR_005317	Version 3.2 used for graph construction and analysis
NumPy Numerical Computing Library	PyData Community	RRID: SCR_008633	Used for numerical operations and array processing
NVIDIA GPU (Tesla T4 / RTX 3080)	NVIDIA Corporation	RRID: SCR_016409	CUDA-enabled hardware accelerator for model training
Pandas Data Analysis Library	PyData Community	RRID: SCR_018214	Used for structured data manipulation
Python Programming Language	Python Software Foundation	RRID: SCR_008394	Version 3.10 used for model development and data processing
PyTorch Deep Learning Framework	Meta AI	RRID: SCR_018536	Version 2.0 used for neural network implementation
Scikit-learn Machine Learning Library	Scikit-learn Developers	RRID: SCR_002577	Version 1.5 used for preprocessing and evaluation metrics
Transformers NLP Library	Hugging Face	RRID: SCR_020989	Version 4.40 used for pretrained transformer models
Ubuntu Linux Operating System	Canonical Ltd.	RRID: SCR_018317	Version 20.04 LTS runtime environment