The Knowledge Graph: Providing Essential Context for AI
A semantic layer that bridges the contextual gap for effective AI and agentic systems
The Contextual Gap in Artificial Intelligence
Raw data, no matter how voluminous, means little without context. An AI system might see a sensor reading of "high flow," but without context, it cannot answer critical operational questions: What assets are upstream of this reading? Which downstream processes will be impacted? What is the standard operating procedure for this specific situation?
For any advanced AI or agentic system to function effectively, it requires more than just access to data; it needs a deep, operational understanding of the relationships between assets, processes, sensors, and rules. This is the contextual gap that generic AI platforms cannot fill. A successful agentic system is defined by both its ability to orchestrate tasks and its access to rich, reliable context.
The Semantic Layer: A Dynamic Model of Your Operations
MAIA's Knowledge Graph, or Semantic Layer, is designed to bridge this gap. It is a fundamentally more powerful and flexible approach for modeling complex industrial environments than traditional relational databases, which are rigid and struggle with diverse data types. The benefits of this approach are substantial:
┌──────────────────────────────────────────────┐
│ MAIA Knowledge Graph (Ontology) │
│ A Dynamic, Queried Model of Operations │
└────────────────────┬─────────────────────────┘
│
┌───────────────────────┼────────────────────────────┐
│ │ │
┌────────▼────────┐ ┌────────▼───────┐ ┌───────▼─────────┐
│ P&IDs, SOPs │ │ Real-Time Tags │ │ Maintenance Logs │
│ (Static Docs) │ │ (Sensors, SCADA)│ │ Work Orders │
└─────────────────┘ └────────────────┘ └──────────────────┘
│ │ │
└────────────┬──────────┴────────────┬───────────────┘
▼ ▼
┌─────────────┐ ┌────────────────┐
│ Assets & │ │ Processes │
│ Components │ │ & Relationships │
└─────────────┘ └────────────────┘
│ │
┌─────────────┼──────────────┬────────┴─────────────┐
│ │ │ │
┌───────▼──────┐┌─────▼──────┐┌──────▼────────┐ ┌───────▼─────┐
│ Pumps ││ Valves ││ Clarifiers │ ... │ BNR System │
│ (Entities) ││ (Entities) ││ (Entities) │ │ (Processes) │
└──────────────┘└────────────┘└────────────────┘ └─────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────────────────────────────────────────────────────────┐
│ Explainable Agentic AI Recommendations (via Graph Traversal) │
│ (e.g., "This pump is downstream of the clarifier showing an │
│ anomaly, was last maintained 7 months ago, and is flagged │
│ due to high temp. SOP recommends XYZ.") │
└─────────────────────────────────────────────────────────────────────┘
◄── Searchable by Operators │ Evolves Over Time │ Industry Standard Ontology ──►Deep, Contextual Queries: The graph structure allows for powerful multi-hop queries that reveal insights impossible to find otherwise. Users can ask complex questions like, "Show me all pumps that are part of the BNR process, were maintained in the last six months, and are downstream from the primary clarifier that is currently showing an anomaly." This also enables enhanced data discovery, allowing engineers to spot previously unknown correlations between different parts of the system.
Flexible Data Integration: Knowledge graphs are built to connect highly diverse and unstructured data. This means a single query can seamlessly link a real-time SCADA tag to its maintenance history in a work order system, a chemical property in a PDF manual, and an operator's log notes about its performance, creating a truly unified view.
Powering Explainable AI (XAI): For operators to trust AI, they need to understand its reasoning. The knowledge graph provides the backbone for explainability. When an agent makes a recommendation, it can show the exact path of data, relationships, and rules it followed, turning a "black box" suggestion into a transparent and trustworthy piece of advice.
Evolvability and Scalability: Water facilities are not static. The graph is designed to evolve. As you add new sensors, upgrade equipment, or change treatment processes, new assets and relationships can be added to the model without requiring a costly and disruptive redesign of the entire database schema.
Crucially, this context map is not just for the AI; it provides a queriable, explorable model of the entire facility for all users, from operators to engineers and managers, functioning as the living institutional memory of your utility.
A Scalable and Standardized Approach to Building Context
Building such a comprehensive knowledge graph from scratch would be a monumental task. Our approach is designed to be both practical and scalable by leveraging the engineering documents that are already standard for every process project in the water sector.
The foundation of the graph is built through the automated ingestion and analysis of documents like Piping and Instrumentation Diagrams (P&IDs) and control loop descriptions. As this information is ingested, the system automatically identifies inconsistencies and mismatches between documents, highlighting areas that require human attention and making the overall management of this critical information far easier.
This entire process is managed through a user-friendly interface that puts utility experts in control. From a central UI, your team can review, edit, and enrich the knowledge graph, progressively adding more structure and detail to the ontology (the formal model of the graph). This methodology is critical for two reasons. First, it makes the creation and maintenance of the knowledge graph a manageable and repeatable process. Second, by building on a flexible yet structured ontology, it creates a clear path toward industry-wide standardization, unlocking the potential for true interoperability.