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This is a Quantitative Study study (also classified as: Experimental Study).

October 16, 2025Open Access

Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems

MNMarvin NiederhausHochschule Bielefeld NMNico MigendaHochschule Bielefeld JWJulian WellerFraunhofer Institute for Mechatronic Systems Design

Key Points

Integrating knowledge graphs enhances interpretability of prescriptive analytics recommendations, enabling better user understanding.
The platform utilizes graph retrieval-augmented generation, improving context-aware decision making based on real user knowledge.
A customer clinic validated the prescriptive platform, demonstrating usability and applicability in a real-world environment.
The methodology included automated workflows for creating knowledge graphs, standardized data sources, and a zero-shot classifier.

Abstract

Making time-critical decisions with serious consequences is a daily aspect of work environments. To support the process of finding optimal actions, data-driven approaches are increasingly being used. The most advanced form of data-driven analytics is prescriptive analytics, which prescribes actionable recommendations for users. However, the produced recommendations rely on complex models and optimization techniques that are difficult to understand or justify to non-expert users. Currently, there is a lack of platforms that offer easy integration of domain-specific prescriptive analytics workflows into production environments. In particular, there is no centralized environment and standardized approach for implementing such prescriptive workflows. To address these challenges, large language models (LLMs) can be leveraged to improve interpretability by translating complex recommendations into clear, context-specific explanations, enabling non-experts to grasp the rationale behind the suggested actions. Nevertheless, we acknowledge the inherent black-box nature of LLMs, which may introduce limitations in transparency. To mitigate these limitations and to provide interpretable recommendations based on real user knowledge, a knowledge graph is integrated. In this paper, we present and validate a prescriptive analytics platform that integrates ontology-based graph retrieval-augmented generation (GraphRAG) to enhance decision making by delivering actionable and context-aware recommendations. For this purpose, a knowledge graph is created through a fully automated workflow based on an ontology, which serves as the backbone of the prescriptive platform. Data sources for the knowledge graph are standardized and classified according to the ontology by employing a zero-shot classifier. For user-friendly presentation, we critically examine the usability of GraphRAG in prescriptive analytics platforms. We validate our prescriptive platform in a customer clinic with industry experts in our IoT-Factory, a dedicated research environment.

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