Construction of Knowledge Graph for Enterprise Mergers and Acquisitions: Cross-Domain Value Mining Method of Large Language Model (LLM) and Graph Neural Network
Year: 2025
Type: article
Abstract: Enterprise mergers and acquisitions (M&A) involve complex, cross-domain decision-making processes that rely on both structured and unstructured data sources. Traditional knowledge graph construction techniques, often rule-based or reliant on shallow learning, struggle with scalability, adaptability, and semantic generalization in such dynamic environments. A new framework for cross-domain value mining is introduced, which integrates Large Language Models and Graph Neural Networks to enable enterprise M&A knowledge graph construction and inference. Central to our approach is the E-GraphNet (Enterprise Graph Network), a modular graph-based neural architecture that models enterprises as asynchronous, multi-agent decision systems. Each node in E-GraphNet represents an enterprise entity, while edges encode organizational dependencies and communication delays. E-GraphNet introduces edge-conditioned message passing and policy-execution signatures to enable dynamic alignment with strategic directives, ensuring real-time and scalable inference across distributed enterprise contexts. To further enhance adaptability under uncertainty and partial observability, we introduce Decision-Aware Perturbation Routing (DAPR). DAPR injects controlled perturbations into decision pathways, simulates distributed decision shifts, and optimizes routing through attention-guided correction mechanisms. This enables the system to remain robust in the face of delayed feedback and external perturbations, improving resilience in real-world M&A scenarios. Evaluation across various enterprise datasets indicates that the LLM-GNN framework delivers superior performance compared to existing baselines in tasks related to knowledge extraction and graph inference. The framework offers a scalable, interpretable, and efficient solution for modeling enterprise structures and forecasting M&A outcomes, advancing the field of intelligent enterprise analytics.
Source: IEEE Access
Author: Lixia Li
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4414647324