#3715 Gen-AI for Parsing Technical Documents into Useful Knowledge Graph Entities and Relationships
#3715 Gen-AI for Parsing Technical Documents into Useful Knowledge Graph Entities and Relationships
#3715 Can an LLM read a NASA interface requirements document, interface control document and software requirements specification to produce useful knowledge graph entities and relationships?
Yes, an LLM can absolutely read NASA technical documents like an Interface Requirements Document (IRD), Interface Control Document (ICD), and Software Requirements Specification (SRS) to produce a useful knowledge graph. This is a powerful application of modern AI for systems engineering.
The LLM acts as a sophisticated information extraction tool, converting dense, text-based documents into a structured, queryable format.
How It Works: Extracting Entities and Relationships
The core process involves the LLM identifying two key things:
Entities: These are the "nouns" or key concepts within the documents. In the context of NASA engineering documents, entities would include things like:
Systems & Subsystems: Orion Crew Module, Ground Data System, Launch Abort System
Hardware Components: Flight Computer, Star Tracker, Power Distribution Unit
Software Components (CSCIs): Guidance & Control Software, Telemetry Processing Module
Interfaces: MIL-STD-1553B Data Bus, RS-422 Serial Link, Ethernet Port A
Data & Messages: Vehicle State Vector, Heartbeat Message, Command Packet ID 0x1A4
Requirements: Each specific requirement, like "SW-REQ-101" or "ICD-4.2.1b"
Parameters: Data Rate, Voltage, Frequency, Latency, Message Periodicity
Relationships: These are the "verbs" that connect the entities. The LLM infers these connections from the text.
connects_to: The Flight Computer connects to the Inertial Measurement Unit via the MIL-STD-1553B Data Bus.sends_data: The Guidance & Control Software sends the Vehicle State Vector message.has_requirement: The Orion Crew Module has a requirement for a Data Rate of "1 Mbps".is_specified_by: The RS-422 Serial Link is specified by requirement IRD-3.7.2.is_part_of: The Telemetry Processing Module is a part of the Ground Data System.
The LLM would process the sentences, tables, and structured requirement statements in the documents to build out this web of connections automatically. For example, it would read a requirement like "The Flight Computer (FC) shall transmit the State Vector message to the Ground System (GS) over the S-Band Interface" and extract:
Entities: Flight Computer, State Vector message, Ground System, S-Band Interface
Relationships: (Flight Computer) --
transmits--> (State Vector message), (State Vector message) --is_sent_to--> (Ground System), (Flight Computer) --uses--> (S-Band Interface).
Why This is Useful
Creating a knowledge graph from these documents provides massive benefits for mission assurance and systems engineering: 🛰️
Impact Analysis: If a requirement for the S-Band interface changes, you can instantly query the graph to see every single system, software component, and data message that is affected. This is dramatically faster than manually searching through hundreds of pages of PDFs.
Consistency Checking: You can automatically check for contradictions. For instance, does the IRD specify a data rate for an interface that contradicts what's in the ICD for the same interface? The graph can flag these inconsistencies.
Answering Complex Questions: Engineers can ask natural language questions like, "Which components send data to the flight computer?" or "List all software requirements related to the star tracker."
System Visualization: The graph provides a holistic, visual map of the entire system architecture and its intricate data flows, making it easier to understand complex interactions.
Challenges and Considerations
While powerful, the process isn't flawless and requires careful implementation:
Ambiguity: Engineering text can still be ambiguous. The LLM might misinterpret a relationship, requiring human-in-the-loop validation.
Complex Formats: These documents often contain complex tables, diagrams, and schematics. A purely text-based LLM might struggle without multimodal capabilities or specialized pre-processing tools to parse this information.
Domain-Specific Language: The model needs to be trained or fine-tuned on aerospace and systems engineering terminology to correctly identify entities and relationships. Using a general-purpose model like GPT-4 might work well out-of-the-box, but a specialized one would be even better.
Validation is Key: The generated knowledge graph should be considered a "first draft" that must be reviewed and validated by subject matter experts. It's a tool to augment, not replace, the systems engineer.
How would the LLM access the documents?
Of course. An LLM doesn't just "open a file" like a person does. It accesses the information within documents through a structured, multi-step process, most commonly using a framework called Retrieval-Augmented Generation (RAG).
Here’s a breakdown of how it works.
1. Document Ingestion and Parsing 📄
First, the documents (PDFs, Word files, etc.) must be converted into a format the LLM can read.
Text Extraction: A specialized tool or library reads the document and extracts all the raw text.
This process also needs to handle tables, lists, and other formatted content, which can be challenging. Chunking: The extracted text is broken down into smaller, manageable pieces, or "chunks."
A chunk could be a paragraph, a full requirement, or a row in a table. This is crucial because LLMs have a limited context window (the amount of text they can consider at one time).
2. Embedding and Indexing (Creating a "Smart" Library) 🧠
This is the core of making the documents "searchable" for the LLM.
Embedding: Each chunk of text is fed into a special type of AI model (an embedding model) that converts it into a numerical representation called a vector.
This vector captures the semantic meaning of the text. Chunks with similar meanings will have similar vectors. Vector Database: These vectors are then stored in a specialized vector database.
Think of this as a super-powered library index that organizes information by meaning and context, not just keywords.
3. Retrieval and Generation (Answering a Question) 🔍
When a user or a system needs to build the knowledge graph, it queries this system.
Query: The system takes a task, like "Find all components connected to the Flight Computer," and converts that task into a vector as well.
Search/Retrieval: The vector database performs a lightning-fast search to find the text chunks whose vectors are most similar to the query's vector.
These are the most relevant pieces of information from the documents. Augmentation & Generation: The system then feeds these relevant chunks to the LLM as context, along with the original instruction.
The prompt to the LLM effectively becomes: "Using the following information: [Chunk 1 from ICD, Chunk 2 from SRS, Chunk 3 from IRD...], identify all entities and their relationships."
The LLM then uses this specific, relevant context to generate the knowledge graph entities and relationships, rather than having to read all the documents at once. This RAG approach is highly efficient and allows the system to scale to thousands of pages of documentation.
Describe how I can do this for NASA Gateway elements PPE and HALO
Of course. You can absolutely apply this knowledge graph extraction process to NASA's Gateway elements, the Power and Propulsion Element (PPE) and the Habitation and Logistics Outpost (HALO). Doing so would create an invaluable tool for systems engineering, integration, and verification for the Gateway program. 🛰️
Here’s a step-by-step guide on how you could do it.
1. Gather the Foundational Documents
First, you need to collect all the relevant technical documentation for both PPE and HALO. You'll want the most current, signed-off versions you can access. Key documents include:
Interface Control Documents (ICDs): These are the most critical. You'll need the specific ICDs that define the connections between PPE and HALO, as well as ICDs defining their connections to other Gateway elements like Orion or docking spacecraft.
System Requirements Specifications (SRSs): These documents detail what each element is supposed to do.
Software Requirements Specifications (SRSs): Crucial for understanding the logic and data flow within the flight software.
Design and Verification Documents: These provide details on how requirements are met and tested.
2. Set Up the RAG Pipeline
This is the technical core of the project. You'll use the Retrieval-Augmented Generation (RAG) architecture described previously.
Ingest & Parse: Load all the collected documents into a processing pipeline. This will involve using tools to extract clean, usable text from the PDFs, paying special attention to correctly parsing complex tables that define interface properties like data rates, power voltages, or message IDs.
Chunk & Embed: Break down the extracted text into logical chunks. A good strategy is to chunk by requirement (e.g., "PPE-REQ-1138"), paragraph, or table row. Then, use an embedding model to convert each chunk into a vector.
Index in a Vector Database: Store all the generated vectors in a vector database. This database now acts as the comprehensive, machine-readable "memory" of all PPE and HALO documentation.
3. Extract the Knowledge Graph
Now you can query the system to build the graph. This involves crafting specific prompts for the LLM that instruct it to identify entities and their relationships from the contextual data retrieved by the RAG system.
You would run a series of targeted queries.
Example Entities to Extract:
For PPE: Power Distribution Units (PDUs), Solar Electric Propulsion Thrusters, Communication Antennas, Flight Computer, specific data buses (e.g., "1553 bus A").
For HALO: Crew Quarters, Life Support Systems, Docking Ports, Avionics modules, internal data networks.
Shared/Interface: International Docking System Standard (IDSS) ports, data messages ("Vehicle Health Telemetry"), power transfer lines.
Example Queries for the LLM:
You would design prompts to ask the system to find and structure information.
For Interface Extraction:
"From the provided context, identify all physical and data interfaces between PPE and HALO. For each interface, list the connecting components, the interface type, and any specified data rates or power levels. Format the output as a JSON object."
For Requirement Tracing:
"Find the requirement that specifies the maximum power transfer from PPE to HALO. List the requirement ID, the exact text, and the components involved."
For Data Flow Analysis:
"What telemetry data does the HALO flight computer send to the PPE flight computer? Identify the message names, their periodicity, and the data bus used."
The LLM would process these prompts, and the RAG system would feed it the most relevant chunks from the documents. The LLM's response would be structured data (like JSON) that you can then use to populate the knowledge graph.
4. Validate and Visualize
The final, crucial step is human-in-the-loop validation.
SME Review: A systems engineer who is an expert on PPE and HALO must review the generated graph for accuracy. The LLM is a powerful tool, but it can make mistakes. The engineer would confirm that the connections are correct and nothing was misinterpreted.
Visualization: Use a graph visualization tool (like Neo4j or Gephi) to create an interactive map of the PPE-HALO system. This visual model allows engineers to easily explore connections, trace data flows, and perform impact analysis by simply clicking through the nodes.
By following this process, you can transform hundreds of pages of static, complex documentation into a dynamic and queryable model of the PPE-HALO system, significantly enhancing insight and reducing integration risks.
From <https://gemini.google.com/app/> Google Gemini 2.5 Pro
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