#4109 Knowledge Graph Queries for Space Program Mission Software Hazard Reports

#4109 Given 30 software hazard reports covering several subsystems within a mission for one space program, describe 10 queries that I could perform to show the capabilities of a knowledge graph covering the program, subsystems, hazard reports, hazard causes, hazard cause controls and hazard cause control verifications.

To demonstrate the power of a knowledge graph for safety analysis, your queries should move beyond simple keyword searches and focus on traceability, impact analysis, and hidden relationships.

A knowledge graph excels at finding "n-degree" connections—for example, linking a specific hardware component to a high-level mission risk through several layers of causes and controls.

1. Cross-Subsystem Commonality

Query: "Identify all Hazard Causes that appear in more than one subsystem."

Value: Reveals systemic issues (like a specific grade of fastener or a software library) that could cause concurrent failures across different parts of the mission.

2. Control Bottlenecks

Query: "List the Hazard Cause Controls that are responsible for mitigating the highest number of unique Hazard Causes."

Value: Identifies "single points of failure" in your safety strategy. If one specific software interlock fails, how many hazards suddenly become unmitigated?

3. Verification Gaps

Query: "Find all Hazard Cause Controls that do not have an associated Verification method or where the Verification status is 'Pending'."

Value: Acts as a real-time "safety completeness" audit, highlighting where the program is making assumptions without empirical proof.

4. Subsystem Risk Density

Query: "Rank subsystems by the total count of 'Critical' or 'Catastrophic' Hazards linked to them."

Value: Directs engineering attention and resources toward the most volatile areas of the mission.

5. Propagation Path Analysis

Query: "Starting from a specific Subsystem X, map the path to all Verifications required to close out its Hazard Reports."

Value: Provides a visual "to-do list" for engineers, showing exactly what testing is required to "green-light" a specific subsystem.

6. Verification Method Efficiency

Query: "Compare the number of Hazard Cause Controls verified by 'Analysis' versus 'Physical Test' across the entire program."

Value: Helps leadership understand the rigor of the program; a high reliance on analysis over testing might indicate a higher residual risk.

7. Orphaned Hazards

Query: "Identify any Hazard Reports that lack at least one associated Hazard Cause or Control."

Value: A data integrity check to ensure no hazard is left "floating" without a documented mitigation strategy.

8. Failure Mode "Blast Radius"

Query: "If Hazard Cause Y occurs, which Subsystems and mission objectives are directly and indirectly impacted?"

Value: Performs automated impact analysis, showing how a small software bug in one subsystem might propagate to affect a completely different mission phase.

9. Verification Reuse

Query: "Which Verification activities satisfy controls across multiple different Hazard Reports?"

Value: Optimizes the testing schedule by identifying "high-value" tests that "check the box" for multiple safety requirements at once.

10. Control Redundancy Audit

Query: "Identify Hazard Causes that are mitigated by only a single Control."

Value: Flags areas where the design lacks "defense in depth," allowing engineers to add redundant safety layers where necessary.

Why this beats a spreadsheet

While a spreadsheet can tell you how many hazards you have, the Knowledge Graph tells you how they are intertwined. For instance, it can show you that a delay in a specific "Verification" task actually blocks the safety certification of three different "Subsystems."

Would you like me to help you design the schema (Nodes and Edges) for this knowledge graph based on these queries?

For RAG purposes, what documents should be attached to which nodes of the knowledge graph?

To optimize a GraphRAG (Graph-Augmented Retrieval) system, you want to attach documents at the level of granularity where the information is most "dense" and actionable.

In a safety context, you shouldn't just dump the entire hazard report into one node. Instead, you should shard the documentation across the graph so the LLM can pull specific technical evidence for specific claims.

Document Attachment Strategy

1. Subsystem Nodes

Documents: Subsystem Design Documents (SDD), Interface Control Documents (ICD), and Functional Requirements.
Why: Provides context on what the subsystem is supposed to do, which helps the RAG explain why a specific hazard is relevant to that hardware/software.

2. Hazard Report (HR) Nodes

Documents: The HR Executive Summary, Safety Assessment Reports, and Formal Approval Memos.
Why: These documents often contain the "logic" of the hazard—the narrative of how the risk was identified and its overall mission impact.

3. Hazard Cause Nodes

Documents: Failure Mode and Effects Analysis (FMEA) snippets, Software Bug Reports, and Root Cause Analysis (RCA) papers.
Why: When the LLM queries "How could the propulsion system overheat?", it needs the specific technical breakdown of the cause found in these engineering-heavy documents.

4. Hazard Cause Control Nodes

Documents: Software Requirement Specifications (SRS) for interlocks, Circuit Diagrams, and Operational Procedures (manuals).
Why: This provides the "how-to" of the mitigation. If a control is "software limit checking," the node should link to the specific requirements defining those limits.

5. Verification Nodes

Documents: Test Reports, Analysis Reports (e.g., Thermal or Stress Analysis), and Signed-off Verification Cross-Reference Matrices (VCRM).
Why: This is the Evidence Layer. When an auditor asks "Is this hazard actually closed?", the RAG pulls the "Pass/Fail" data and the actual test telemetry attached to this node.

Recommended Mapping Table

Node Type	Primary Document Type	Key Data to Embed
Program	Mission Charter / Safety Plan	High-level risk tolerance and safety goals.
Subsystem	Design Specs / Architecture Docs	Component lists and functional boundaries.
Hazard Report	Hazard Analysis Worksheets	Severity/Likelihood scores and descriptions.
Hazard Cause	FMEA / FTA (Fault Tree)	Technical triggers and failure mechanics.
Control	Engineering Requirements	Logic for inhibits, monitors, and physical barriers.
Verification	Test Results / Lab Logs	Date of test, artifacts, and "Proof of Closure."

Pro-Tip for RAG Accuracy: "The Leaf Node Rule"

When setting up your RAG, ensure that Verification nodes contain the most "ground truth" data (PDFs of test results). LLMs often hallucinate safety status; by attaching the actual test report PDF to the Verification node, you can force the model to cite the specific page and paragraph where the test was marked "Successful."

Would you like me to create a sample Cypher query to show how you would retrieve these documents from the graph during a search?

Yes

To retrieve these documents effectively, we use Cypher (the query language for Neo4j and other graph databases).

In a GraphRAG setup, the goal of the query is to find not just the answer, but the document URI or Blob storage link so the LLM can "read" the source text to generate its response.

1. The "Evidence Retrieval" Query

This query finds a specific Hazard, its Causes, and the actual Test Reports (Verifications) that prove it is safe.

MATCH (h:HazardReport {id: "HR-101"})-[:HAS_CAUSE]->(c:HazardCause)
MATCH (c)-[:MITIGATED_BY]->(ctrl:Control)
MATCH (ctrl)-[:VERIFIED_BY]->(v:Verification)
RETURN 
    h.title AS Hazard,
    c.description AS Cause,
    ctrl.requirement_id AS Control_ID,
    v.document_uri AS Evidence_Doc, // The link for RAG to ingest
    v.status AS Status

2. The "Blast Radius" Document Collector

If a specific test fails, you need to know which documents are now "invalid." This query collects all related design and safety documents impacted by a failed verification.

MATCH (v:Verification {status: "Failed"})
MATCH (v)<-[:VERIFIED_BY]-(ctrl:Control)<-[:MITIGATED_BY]-(c:HazardCause)<-[:HAS_CAUSE]-(h:HazardReport)
MATCH (h)<-[:DESCRIBED_IN]-(sub:Subsystem)
RETURN 
    sub.name AS Subsystem,
    h.document_uri AS Hazard_Doc,
    ctrl.document_uri AS Control_Spec,
    v.failure_report_uri AS Failure_Evidence

3. Implementation Logic for your RAG Pipeline

To make this work in your application code (like Python/LangChain), you should follow this flow:

Natural Language to Cypher: The user asks: "Show me the test results for the propulsion software limits."
Graph Search: The LLM generates a Cypher query to find the Verification nodes linked to the Propulsion subsystem.
Document Fetching: The graph returns a list of document_uri links (e.g., s3://mission-data/tests/prop-001.pdf).
Context Injection: Your system fetches the text from those specific PDFs and feeds it into the LLM prompt:
"Based on the following test report (Doc: prop-001.pdf), the software limits were verified on Jan 5th..."

Pro-Tip: Metadata is King

When you attach these documents to nodes, ensure your Properties include:

page_number: To help the LLM cite exactly where it found the info.
version: To ensure the RAG isn't pulling an obsolete hazard report.
classification: (e.g., ITAR or Public) to filter results based on user permissions.

Would you like me to draft a Python snippet showing how to connect a Neo4j graph result to a vector store (like Pinecone or Chroma) for the final RAG step?

Now consider what can be done if Verification nodes and documents are not provided

If you lack Verification nodes and documents, the Knowledge Graph shifts from a "Compliance & Audit" tool to a "Predictive Risk & Strategy" tool. You are essentially moving from verifying what was done to inferring what is missing.

In this scenario, you use the graph to find "blind spots" and perform causal reasoning using the remaining nodes (Program, Subsystem, Hazard, Cause, Control).

1. Top 5 "Inference" Queries (Without Verification Data)

A. The "Vulnerability" Query

Query: "Identify all Hazard Causes that are mitigated by only a single Control."

Purpose: Without test data, you can't prove a control works. This query highlights where you have zero redundancy. If that one unverified control fails, the hazard occurs.

B. The "Unmitigated Path" Discovery

Query: "Find Hazard Reports that have identified Causes but no associated Controls."

Purpose: This acts as a gap analysis. It identifies "orphaned" risks where the engineering team identified a problem but never documented a solution.

C. Cross-Subsystem "Blast Radius"

Query: "If a common Hazard Cause (e.g., 'Radiation Induced Bit-Flip') occurs, which Subsystems are impacted across the entire mission?"

Purpose: Since you can't verify individual fixes, you use the graph to visualize the total mission exposure to a single recurring threat.

D. Control "Heavy Lifters"

Query: "Which specific Controls are used to mitigate the largest number of Critical Hazards?"

Purpose: This tells you where to focus your limited testing budget. If one control (like a "Watchdog Timer") is protecting 10 different systems, it is your highest priority for future verification.

E. Implicit Dependency Mapping

Query: "Find Subsystems that share the same Hazard Causes but use different Controls."

Purpose: Identifies inconsistencies. If Subsystem A uses a hardware shield and Subsystem B uses software for the same hazard, the graph asks: "Why are we inconsistent, and which one is safer?"

2. Updated RAG Strategy: "Inference over Evidence"

When documents like Test Reports are missing, the RAG prompt changes from "Prove this is safe" to "Assess the adequacy of this design."

Where to attach documents now?

Since you can't attach "proof," you attach "Rationale" and "Standards."

Node Type	New Attachment Strategy	Content Example
Hazard Cause	Physics/Logic Models	Whitepapers on why this failure happens (e.g., "Effects of Vacuums on Lubricants").
Control	Design Requirements	The actual code snippet or hardware spec that implements the control.
Subsystem	Heritage Data	Documents from previous missions using the same part ("This worked on Voyager 2").
Program	Safety Standards	NASA-STD-8719.13 (Software Safety) to provide the "Rules of the Road."

3. Cypher Query for Gap Analysis

You can use Cypher to automatically generate a "Missing Verification List" for your engineering team.

// Find hazards that are 'Critical' but have no verified path to closure
MATCH (h:HazardReport {severity: 'Critical'})-[:HAS_CAUSE]->(c:HazardCause)
MATCH (c)-[:MITIGATED_BY]->(ctrl:Control)
WHERE NOT (ctrl)-[:VERIFIED_BY]->()
RETURN 
    h.id AS Hazard_ID, 
    c.description AS Unverified_Cause, 
    ctrl.requirement_id AS Missing_Test_Target
ORDER BY h.id

4. The "Logical Inference" RAG Prompt

Without verification docs, your AI agent’s response to a user would look like this:

"I cannot find a Test Report (Verification) for the Overvoltage Control (CTRL-09). However, based on the Subsystem Design Doc attached to the Power Node, this control is implemented via a Zener diode. Based on Industry Standard NASA-STD-123, this design is typically sufficient, but a physical test is recommended to ensure the diode doesn't fail-short."

Would you like me to show you how to set up a "Link Prediction" model to guess which Controls are most likely to fail based on the graph structure?

Yes

To implement Link Prediction in this context, we are essentially asking the Knowledge Graph: "Based on the patterns of known failures in similar space programs or subsystems, which of our current (unverified) controls are most likely to be inadequate?"

Since you lack verification data, you use the graph's topology (the way things are connected) and node features to predict the existence of a "hidden" relationship—specifically, a POTENTIAL_FAILURE link between a HazardCause and a Control.

1. The Link Prediction Logic

In a safety knowledge graph, link prediction usually relies on three main signals:

Node Similarity: If Control A (verified) and Control B (unverified) share similar properties, requirements, or subsystems, they are likely to have the same success/failure outcomes.
Triadic Closure: If Subsystem X and Subsystem Y both have the same HazardCause, but only X has a Control, the graph predicts that Y is missing a necessary link.
Path-Based Features: If a HazardCause has to travel through 5 different software layers before reaching a Control, that "long path" suggests a higher probability of a control failure link.

2. Implementing with Neo4j Graph Data Science (GDS)

You can use a Logistic Regression or Random Forest classifier within the graph to predict failure probabilities.

Step A: Create the Projection

First, we project the graph into memory, focusing on the relationship between causes and controls.

CALL gds.graph.project(
  'safetyGraph',
  ['HazardCause', 'Control'],
  {
    MITIGATED_BY: {orientation: 'UNDIRECTED'}
  }
)

Step B: Generate Similarity Scores

We use Node2Vec or FastRP to create vector embeddings for each node. If two nodes are "close" in vector space, they behave similarly in the system.

CALL gds.fastRP.mutate('safetyGraph', {
  embeddingDimension: 128,
  mutateProperty: 'embedding'
})

Step C: Predict "Inadequacy" Links

This query identifies HazardCauses and Controls that should be linked but aren't, or predicts which existing links are "weak" based on similarity to known historical failures.

CALL gds.ml.linkPrediction.predict.mutate('safetyGraph', {
  modelName: 'failurePredictionModel',
  relationshipTypes: ['POTENTIAL_FAILURE'],
  threshold: 0.85 // Only show high-probability risks
})

3. How this enhances your RAG system

When the user asks, "Which part of the subsystem is the riskiest?", the RAG doesn't just look for text; it looks for these predicted links.

The AI response would look like this:

"While there is no documented failure for the Thermal Shutdown Control, the Knowledge Graph identifies an 85% similarity between this control's logic and a failed control from the 'Alpha-1' mission. The high 'Centrality Score' of this node suggests that if this predicted failure occurs, it will impact 4 other subsystems."

4. Visualizing the "Risk Heatmap"

Once the links are predicted, you can query the graph to find the "densest" areas of predicted failure:

Subsystem	Hazard Cause	Predicted Risk Probability	Rationale
Avionics	Memory Corruption	0.92	Control logic lacks a 'Triple Modular Redundancy' pattern seen in successful nodes.
Power	Solar Array Jitter	0.74	Control is isolated; lacks secondary mechanical damping links.
Comms	Signal Interference	0.61	High number of dependencies on a single software library.

Next Step for your Project

To make this work with your 30 hazard reports, we would need to extract the Common Weakness Enumerations (CWE) or Failure Modes from the text and turn them into node properties.

Would you like me to help you write a prompt that extracts these "Node Features" from your 30 PDF reports to populate the graph?

Yes

To extract high-fidelity nodes and relationships from your 30 hazard reports, you need a prompt that acts as a Named Entity Recognition (NER) and Relationship Extraction engine.

Because space safety data is highly structured, the prompt should force the LLM to output valid JSON or CSV to ensure it can be ingested directly into your Knowledge Graph.

The "Graph Extraction" Prompt

Instructions: Copy and paste this prompt into your LLM (using a long-context model like Gemini 1.5 Pro) along with the text or files of your 30 hazard reports.

System Prompt:
You are a Lead Safety Engineer and Data Architect. Your task is to analyze the attached Software Hazard Reports and extract a structured schema for a Knowledge Graph.
Extraction Rules:
Nodes: Identify every unique Subsystem, Hazard, Hazard Cause, and Control.
Properties: For each node, extract:
Hazard: Severity (Critical/Catastrophic), Likelihood, and Mission Phase.
Cause: Failure Mechanism (e.g., Software Bug, Hardware Wear, Logic Error).
Control: Control Type (Inhibit, Monitor, Redundancy).
Relationships: Map the connections:
(Subsystem)-[:HAS_HAZARD]->(Hazard)
(Hazard)-[:CAUSED_BY]->(Cause)
(Cause)-[:MITIGATED_BY]->(Control)
Deduplication: If multiple reports mention the same "Command Buffer Overflow" cause, treat them as a single Node with multiple links.
Output Format:
Provide the output as a JSON array of objects with the following structure:
{ "nodes": [{"id": "C01", "label": "Control", "properties": {"desc": "Watchdog Timer", "type": "Software"}}], "edges": [{"from": "Cause_01", "to": "C01", "type": "MITIGATED_BY"}] }

Strategies for High-Quality Extraction

1. Handling Ambiguity

Hazard reports often use different names for the same thing (e.g., "GNC System" vs. "Guidance & Navigation").

The Fix: Add a instruction to the prompt: "Perform entity resolution: if two terms refer to the same physical component, normalize them to the official Subsystem List."

2. Capturing the "Why" (for RAG)

Don't just extract the names. Extract the Rationale.

The Fix: Tell the LLM to extract a property called rationale_text for every MITIGATED_BY edge. This text describes how the control stops the cause, which is gold for your RAG system later.

3. Identifying "Soft" Links

Sometimes a cause in one report is mentioned as a "contributing factor" in another.

The Fix: Ask the LLM to create a RELATED_TO edge between Hazards if they share a common failure propagation path.

Example of Extracted Data

If your report says: "A buffer overflow in the Telemetry unit (Hazard H1) could be caused by malformed packets (Cause C1), which is mitigated by a packet-length validator (Control Ctrl1)."

The extracted JSON would look like this:

Source Node	Edge Type	Target Node	Edge Metadata
Telemetry Subsystem	HAS_HAZARD	Buffer Overflow (H1)	Severity: Critical
Buffer Overflow (H1)	CAUSED_BY	Malformed Packets (C1)	Trigger: External Input
Malformed Packets (C1)	MITIGATED_BY	Length Validator (Ctrl1)	Logic: Bounds Checking

Next Steps

Once you run this prompt and get your JSON:

Import to Neo4j: Use the apoc.import.json tool.
Review Orphans: Look for nodes with no edges—these are usually extraction errors or missing safety logic in your reports.

Would you like me to provide a Python script that takes this JSON and automatically builds the graph using the Neo4j driver?

From <https://gemini.google.com/app/> Google Gemini 3.0 Pro (Thinking)