A Survey of Architectures for Multimodal Industrial Knowledge Bases

Created by: Zifeng Last Updated: November 26, 2025

TL;DR: The state-of-the-art for industrial knowledge bases is rapidly converging on hybrid architectures that fuse the structured reasoning of Knowledge Graphs (KGs) with the semantic flexibility of Retrieval-Augmented Generation (RAG), enabling complex, multimodal queries across previously siloed data like PDFs, CAD files, and sensor logs.

Keywords: #MultimodalAI #KnowledgeGraph #RetrievalAugmentedGeneration #IndustrialAI #Industry4.0 #SemanticSearch

❓ The Big Questions

The quest to transform vast, heterogeneous industrial data into actionable intelligence is driven by several fundamental research questions that echo across the surveyed literature:

1. How can we effectively unify disparate data modalities into a single, cohesive knowledge model? Industrial environments are rich with unstructured PDFs, structured CAD files, real-time sensor data, and procedural videos. A central challenge, explored in works by (Tian et al., 2025), (Liu & Lu, 2024), and survey papers like (Zhu et al., 2022), is how to move beyond simple data lakes and create a unified representation that captures the intricate relationships between these different forms of knowledge.

2. What is the optimal architecture for balancing structured reasoning and flexible semantic search? A key debate is emerging between purely semantic systems and structured knowledge systems. Papers like (Meister et al., 2025) and (Wan et al., 2025) demonstrate that the most powerful solutions are hybrid. How do we best combine the explicit, verifiable reasoning of Knowledge Graphs with the powerful natural language understanding and unstructured data handling of Large Language Model (LLM)-based RAG systems?

3. How can we guarantee the accuracy, trustworthiness, and explainability of AI-generated insights in high-stakes industrial settings? In manufacturing, aerospace, and energy, a "hallucinated" answer can have catastrophic consequences. This question drives research into ontology-based grounding (Naqvi et al., 2025; Unknown, 2025), rule-based RAG (Xiong et al., 2025), and robust evaluation frameworks (AWS, 2025) to ensure that AI-generated responses are not just plausible, but factually correct and traceable to their source.

4. How can these complex systems be scaled to handle the velocity, volume, and variety of real-world industrial data while remaining computationally and financially viable? Many cutting-edge approaches are demonstrated on limited datasets and acknowledge scalability as a major hurdle. The works of (Varughese, 2025) and (Shi et al., 2025) touch upon the significant computational costs and deployment challenges, highlighting the need for more efficient models, indexing strategies, and infrastructure to make these systems practical at an industrial scale.

🔬 The Ecosystem

The research landscape for multimodal industrial knowledge bases is vibrant and interdisciplinary, drawing from fields like knowledge engineering, natural language processing, computer vision, and domain-specific engineering.

Key Research Clusters & Institutions: The field is heavily driven by contributions published in top-tier engineering and computer science venues. Journals like ScienceDirect's Advanced Engineering Informatics, Computers in Industry, and Journal of Industrial Information Integration, along with conferences and archives from IEEE and ASME, are epicenters of this research. Corporate research arms, such as IBM (Varughese, 2025) and AWS (2025), are also pushing the boundaries, particularly in the practical application and evaluation of RAG systems. Academic research groups are frequently represented, with authors like Dachuan Shi, Jianzhang Li, Olga Meyer, & Thomas Bauernhansl (2025) focusing on interoperability with standards like the Asset Administration Shell, and Xirui Xiong, Hongming Cai, et al. (2025) pioneering domain-specific, rule-based systems for aerospace.

Pivotal Papers & Concepts: Several papers in this collection represent key conceptual shifts:

• The Hybrid Imperative: The work by Meister et al. (2025) on "Retrieval-Augmented Generation using Knowledge Graphs for Manufacturing Problem-Solving" serves as a prime example of the prevailing hybrid trend. It demonstrates that combining KGs for causal inference with RAG for semantic search yields superior performance in complex tasks like fault diagnosis. Similarly, Wan et al. (2025) show that a hybrid KG-Vector RAG outperforms single-paradigm approaches in smart manufacturing Q&A.
• The Multimodal Frontier: The "RAG-Anything" framework by Guo et al. (2025) and IBM's explainer on "What is multimodal RAG?" (Varughese, 2025) signal the definitive move beyond text. The ability to process and reason over documents containing text, images, tables, and equations in a unified manner is becoming a baseline expectation. The work of Liu & Lu (2024), which extracts knowledge from text, layout, and visual information in PDF manuals, is a concrete implementation of this principle.
• The Need for Structure and Rules: While LLMs offer flexibility, papers like "DR-RAG" by Xiong et al. (2025) argue for integrating explicit domain rules to guide retrieval and generation, especially in safety-critical domains like aviation. This is complemented by research from Pokojski et al. (2022) on knowledge-based engineering in CAD systems, which underscores the long-standing value of formal, structured knowledge representation.
• The Evaluation Gap: A parallel stream of literature, including contributions from AWS (2025) and a host of blog posts (Gupta, 2024; Heath, 2021), highlights a critical challenge: how do we measure success? The field is moving from simple usage metrics (e.g., page views) to sophisticated, multi-faceted evaluations of RAG systems, assessing context relevance, faithfulness, and answer correctness.

🎯 Who Should Care & Why

The advancements detailed in this literature have profound implications for a wide range of stakeholders:

• Industrial Practitioners (Engineers, Technicians, Plant Managers): This research directly addresses daily operational pain points. Instead of manually sifting through thousands of pages of manuals or disparate databases, they can use natural language to ask complex questions like, "What were the root causes of bearing failure on CNC machine #7 in the last year, and what were the most effective corrective actions?" This leads to faster fault diagnosis (Wu et al., 2023; Zhuang et al., 2025), improved quality control (Heredia Álvaro & González Barreda, 2025), and more efficient maintenance planning (Liu & Lu, 2024).
• AI/ML Researchers & Data Scientists: The industrial domain presents a rich, high-impact "real-world" laboratory. It offers complex, multimodal datasets and poses unique challenges that drive innovation in areas like:
  • Hybrid Architectures: Designing novel ways to fuse KGs and LLMs.
  • Multimodal Fusion: Developing techniques for joint embeddings and cross-modal attention that work on noisy, varied industrial data.
  • Domain-Specific Evaluation: Creating benchmarks and metrics that capture the nuances of industrial accuracy and reliability.
• Knowledge Management & IT Professionals: The era of static, folder-based intranets is over. This research provides a blueprint for the next generation of enterprise knowledge systems. It shows how to build dynamic, "living" knowledge bases that are automatically populated, context-aware, and accessible via intelligent search, fundamentally changing how organizations capture and leverage institutional knowledge.
• Platform & Software Developers: There is a massive commercial opportunity to build the platforms that power these industrial knowledge bases. Frameworks like "RAG-Anything" (Guo et al., 2025) are open-source precursors to what will likely become robust enterprise solutions for creating, managing, and querying multimodal knowledge graphs.

✍️ My Take

This body of literature paints a clear picture of a field in rapid, exciting transition. We are moving decisively away from siloed approaches and toward integrated, multimodal, and intelligent systems. My synthesis of these 36 papers reveals several key insights and future trajectories:

The Unstoppable Rise of the KG-RAG Hybrid: The central thesis emerging from this collection is that the future of industrial knowledge is hybrid. Pure vector-based RAG, while powerful for semantic search over text, struggles with the complex, relational, and causal reasoning required in industrial settings. Pure KG systems, while excellent for structured queries, are often brittle and struggle with the ambiguity of natural language and unstructured data. The convergence, as seen in papers by Meister et al. (2025), Wan et al. (2025), and Xiong et al. (2025), is the KG-RAG architecture. In this paradigm, the KG acts as a structured "world model" or reasoning backbone, providing entities, relationships, and constraints. The RAG system serves as the flexible, multimodal interface, translating natural language queries, retrieving relevant context from both the KG and unstructured documents (via vector search), and generating coherent, evidence-based answers.

From Text-Centric to Truly Multimodal: The second major trend is the expansion of the "retrieval" component to encompass all relevant modalities. Early RAG focused on text chunks. The state-of-the-art, as outlined by Varughese (2025) and demonstrated by Liu & Lu (2024) and Guo et al. (2025), now involves creating a unified embedding space where text from a manual, a specific region in a CAD drawing, a frame from a maintenance video, and a time-series anomaly from a sensor can all be represented and retrieved based on semantic similarity. This is the key to answering truly complex industrial queries that span multiple data sources.

The Enduring Debate: Formalism vs. Automation: A healthy tension exists between approaches that prioritize formal, expert-driven knowledge structures and those that leverage LLMs for large-scale automation. * The Formalist Camp: Argues for the necessity of ontologies (Naqvi et al., 2025), rule engines (Xiong et al., 2025), and standardized models like the Asset Administration Shell (Shi et al., 2025). Their strength lies in precision, verifiability, and explainability—critical for industrial applications. The drawback is the manual effort and expertise required. * The Automation Camp: Leverages LLMs for end-to-end knowledge extraction and graph construction from raw documents (Tian et al., 2025). This approach offers unparalleled scale and speed but can introduce noise and errors, and the reasoning process is less transparent.

The most promising future direction is a synthesis of the two: using LLMs to semi-automate the population of formal, expert-validated ontologies and KGs. This combines the scalability of LLMs with the reliability of structured knowledge.

Future Directions & Research Gaps:

1. Standardization for Interoperability: The work on Asset Administration Shells (Shi et al., 2025) is a critical first step. The industry needs more standards for representing multimodal knowledge to prevent the creation of thousands of bespoke, incompatible systems.
2. Robust Multimodal Evaluation: The current evaluation literature is split. We have high-level business metrics for traditional KBs (Murphy, 2024) and increasingly sophisticated metrics for text-based RAG (AWS, 2025). A significant gap exists for a unified benchmark that evaluates multimodal, KG-enhanced RAG systems on industrial tasks. This benchmark must measure not just retrieval precision but the accuracy of multimodal grounding, the correctness of causal reasoning, and the ultimate impact on a real-world industrial KPI.
3. Scalability and Cost-Effectiveness: The elephant in the room is cost. Training, fine-tuning, and running inference on large multimodal models and graph databases is expensive. Future research must focus on model quantization, efficient indexing techniques (beyond simple vector FAISS), and distributed architectures that can operate at industrial scale and speed without prohibitive costs.
4. Human-in-the-Loop and Explainability: For trust and safety, these systems cannot be black boxes. Future architectures must have explainability built-in. KG-based reasoning paths are a natural starting point. RAG systems must provide clear, verifiable citations for every piece of information used in a generated answer. Furthermore, creating intuitive human-in-the-loop interfaces for experts to easily validate, correct, and enrich the knowledge base will be crucial for long-term success and continuous improvement.

📚 The Reference List