#3677 Hazard Report Knowledge Graph Ontology

#3677 I want to create a NASA software hazard report knowledge graph. Suggest the ontology

Of course. I can help you structure an ontology for a NASA software hazard report knowledge graph. This ontology will serve as the blueprint for the types of entities, their properties, and the relationships between them.

Here is a suggested ontology, presented in a clear, structured format.

Ontology for NASA Software Hazard Report Knowledge Graph

This document outlines a proposed ontology for creating a knowledge graph from NASA's software hazard reports. The goal is to structure the information to enable advanced querying, pattern analysis, and risk assessment.

1. Core Classes (The "Nouns")

These are the primary entities or concepts in the domain.

1.1. HazardReport

The central entity representing a single hazard report document.

Description: A formal document submitted to identify and describe a potential software hazard.
Example: "SHR-2024-08-15-001: Risk of buffer overflow in telemetry processing unit."

1.2. Hazard

The abstract concept of the hazard itself. A report describes a hazard.

Description: A condition or event with the potential for causing an undesirable consequence.
Example: "Buffer Overflow," "Race Condition," "Deadlock."

1.3. SoftwareSystem

The specific software component, module, or system to which the hazard applies.

Description: A distinct software unit with a defined function.
Example: "Flight Guidance Software v3.2," "Data Archiving Module," "Attitude Control System."

1.4. Mission

The overarching NASA mission that the software system is a part of.

Description: A major, long-term endeavor with a specific set of scientific or exploration goals.
Example: "Artemis III," "Mars Science Laboratory," "Europa Clipper."

1.5. Cause

The root cause or contributing factor that leads to the hazard.

Description: The specific error, flaw, or condition that gives rise to the hazard. Can be sub-classed into SoftwareBug, HardwareFault, ProceduralError.
Example: "Improper input validation," "Incorrect memory allocation."

1.6. Consequence

The potential negative outcome if the hazard is realized.

Description: The effect or impact on the system, mission, or personnel.
Example: "Loss of Telemetry Data," "System Crash," "Incorrect Trajectory Calculation."

1.7. Mitigation

A recommended or implemented action to prevent or reduce the likelihood/severity of the hazard.

Description: A safeguard, procedure, or design change intended to control the risk.
Example: "Add input sanitization routine," "Implement watchdog timer," "Revise pre-flight checklist."

1.8. Person

An individual involved in the hazard reporting process.

Description: Can represent the submitter, an analyst, an engineer, or a manager.
Example: "Jane Doe (Submitter)," "John Smith (Safety Analyst)."

2. Properties (The "Verbs" and "Adjectives")

These define the attributes of the classes and the relationships between them.

2.1. Datatype Properties (Attributes of a single class)

For HazardReport:
- reportID (String): The unique identifier for the report.
- reportTitle (String): The title of the report.
- creationDate (Date): The date the report was submitted.
- status (String): The current status (e.g., "Open," "Under Review," "Mitigated," "Closed").
- detailedDescription (String): The full text description from the report.
For Hazard:
- severity (String/Integer): A classification of the worst-case potential consequence (e.g., "Catastrophic," "Critical," "Marginal").
- likelihood (String/Integer): The estimated probability of the hazard occurring (e.g., "Frequent," "Probable," "Occasional," "Remote").
For SoftwareSystem:
- version (String): The version number of the software.
- language (String): The programming language used (e.g., "C++," "Python," "Ada").

2.2. Object Properties (Relationships between classes)

HazardReport describes Hazard
HazardReport submittedBy Person
HazardReport analyzedBy Person
Hazard affects SoftwareSystem
Hazard hasCause Cause
Hazard hasPotentialConsequence Consequence
Hazard isMitigatedBy Mitigation
Mitigation appliesTo SoftwareSystem
Mitigation verifies Requirement (Could be another class for formal requirements)
SoftwareSystem partOf Mission
SoftwareSystem runsOn Hardware (Another potential class for hardware components)

3. Example Usage (A Simple Graph)

Here's how these classes and properties would connect for a specific report:

[HazardReport: SHR-001] --(describes)--> [Hazard: Race Condition]
[HazardReport: SHR-001] --(submittedBy)--> [Person: J. Doe]
[Hazard: Race Condition] --(affects)--> [SoftwareSystem: FSW v1.4]
[Hazard: Race Condition] --(hasCause)--> [Cause: Missing Mutex Lock]
[Hazard: Race Condition] --(hasPotentialConsequence)--> [Consequence: Data Corruption]
[Hazard: Race Condition] --(isMitigatedBy)--> [Mitigation: Add Semaphore]
[SoftwareSystem: FSW v1.4] --(partOf)--> [Mission: Artemis III]

This ontology provides a solid foundation. You can now use this model to structure your data, whether you're using a graph database like Neo4j, a triple store with RDF and SPARQL, or another knowledge graph platform.

Let me know if you'd like to dive deeper into any of these areas, such as defining sub-classes (e.g., different types of Cause) or exploring more complex relationships!

Actual software hazard reports are published as PDF files. How could this data be ingested into the knowledge graph?

Excellent question. Moving from unstructured PDF documents to a structured knowledge graph is a classic and crucial data engineering challenge. This process is often called an ETL (Extract, Transform, Load) pipeline.

Here is a comprehensive breakdown of the steps and technologies you could use to ingest data from NASA's PDF hazard reports into your knowledge graph, based on the ontology we defined.

The Ingestion Pipeline: A 3-Step Process

The entire workflow can be visualized as a pipeline that takes a PDF as input and outputs nodes and relationships in your graph database.

Licensed by Google

Let's break down each step.

Step 1: Extract Raw Content from PDFs

The first goal is to get all the text and structured information out of the PDF file format. PDFs are designed for presentation, not data, so this can be tricky.

For Text Extraction: You'll use libraries to parse the PDF and pull out raw text blocks. If the PDFs are scanned images, you'll need Optical Character Recognition (OCR).
- Tools: Python is the standard for this.
  - PyPDF2 or pdfplumber: Excellent for digitally native PDFs. pdfplumber is particularly good because it can understand the position of text, which helps in identifying fields.
  - Tesseract (with pytesseract wrapper): The go-to open-source OCR engine for scanned PDFs.
For Table Extraction: Hazard reports often contain tables for risk matrices (severity vs. likelihood) or mitigation steps. These need special handling.
- Tools:
  - tabula-py: A powerful library specifically designed to read tables from PDFs and convert them into a structured format like a CSV or a Python DataFrame.
  - camelot: Another excellent table extraction library with more configuration options for tricky layouts.

Output of this step: A raw text file (or JSON object) containing the document's content, plus separate structured files (like CSVs) for any tables.

Step 2: Transform and Structure the Data (The NLP/AI Core)

This is the most critical step. You need to turn the raw text into structured information that matches your ontology. You can use a combination of methods, from simple to highly advanced.

Method A: Rule-Based & Pattern Matching (Regex)

This is best for highly predictable, structured information.

How it works: You write regular expressions (regex) to find and extract specific pieces of data.
Best for:
- reportID: e.g., SHR-\d{4}-\d{2}-\d{2}-\d{3}
- creationDate: e.g., Date:\s*(\w+\s\d{1,2},\s\d{4})
- SoftwareSystem and version: e.g., System:\s*([\w\s]+)\s*v([\d\.]+)
- status: Keywords like "Status: Open" or "Status: Closed"

Method B: Named Entity Recognition (NER)

This is a machine learning approach to find and classify key entities within the unstructured descriptive text.

How it works: You train or use a pre-trained model to identify your core classes within sentences.
Best for: Finding mentions of Hazard, Cause, Consequence, and Mitigation in the descriptive sections.
Example:
- Input Text: "A race condition in the flight guidance software could lead to data corruption due to a missing mutex lock."
- NER Output:
  - Hazard: "race condition"
  - SoftwareSystem: "flight guidance software"
  - Consequence: "data corruption"
  - Cause: "missing mutex lock"
Tools:
- spaCy: A very popular and user-friendly NLP library. You can train a custom NER model to recognize your specific entities.
- Hugging Face Transformers: Provides access to state-of-the-art models (like BERT, RoBERTa) that you can fine-tune on your own labeled data for higher accuracy.

Method C: Large Language Models (LLMs) with Structured Output

This is the most modern and powerful approach. You can use an LLM like Gemini to read the text and directly generate a JSON object that matches your ontology's structure.

How it works: You feed the extracted text into the LLM API with a carefully crafted prompt that includes the desired JSON schema.

Example Prompt:

Given the following text from a NASA software hazard report, extract the relevant information and format it as a JSON object with the following keys: "hazard_type", "affected_system", "potential_consequence", "root_cause", and "suggested_mitigation".

Text: """
[Paste the extracted text from the PDF's description section here]
"""

Advantages: This method excels at understanding context and can handle variations in language and sentence structure much better than regex or even traditional NER. It's also great for Relationship Extraction (e.g., determining that this cause leads to that hazard).

Output of this step: A structured JSON object for each PDF report. This JSON is the bridge between the unstructured document and the structured graph.

{
  "reportID": "SHR-2024-08-15-001",
  "creationDate": "2024-08-15",
  "status": "Open",
  "hazard": {
    "name": "Buffer Overflow",
    "severity": "Critical",
    "likelihood": "Occasional"
  },
  "softwareSystem": {
    "name": "Telemetry Processing Unit",
    "version": "2.1"
  },
  "mission": "Europa Clipper",
  "cause": "Improper input validation",
  "consequence": "Loss of telemetry data",
  "mitigation": "Add input sanitization routine"
}

Step 3: Load Data into the Knowledge Graph

With the clean, structured JSON, you can now populate your graph database.

How it works: You'll write a script that reads the JSON and executes queries to create the nodes and relationships. The key is to use MERGE (or CREATE with checks) to avoid creating duplicate nodes.

Example (using Cypher for Neo4j):

// Load data from the JSON object
WITH $jsonData AS data

// Use MERGE to create or find the HazardReport node
MERGE (report:HazardReport {reportID: data.reportID})
ON CREATE SET report.creationDate = data.creationDate, report.status = data.status

// MERGE other nodes to avoid duplicates
MERGE (h:Hazard {name: data.hazard.name})
ON CREATE SET h.severity = data.hazard.severity, h.likelihood = data.hazard.likelihood

MERGE (s:SoftwareSystem {name: data.softwareSystem.name, version: data.softwareSystem.version})
MERGE (m:Mission {name: data.mission})
MERGE (c:Cause {name: data.cause})
MERGE (cons:Consequence {name: data.consequence})
MERGE (mit:Mitigation {name: data.mitigation})

// Create the relationships between the nodes
MERGE (report)-[:DESCRIBES]->(h)
MERGE (h)-[:AFFECTS]->(s)
MERGE (s)-[:PART_OF]->(m)
MERGE (h)-[:HAS_CAUSE]->(c)
MERGE (h)-[:HAS_POTENTIAL_CONSEQUENCE]->(cons)
MERGE (h)-[:IS_MITIGATED_BY]->(mit)

Summary & Recommended Approach

A robust pipeline will use a hybrid approach:

Extract: Use pdfplumber for text and tabula-py for tables.
Transform:
- Use Regex for simple, fixed-format fields (ID, date).
- Use an LLM (like Gemini) to process the large, unstructured text blocks (description, analysis, cause) to extract entities and their relationships into a structured JSON. This is generally more flexible and powerful than training a custom NER model from scratch.
Load: Write a script to iterate through your JSON files and execute MERGE queries in your graph database to build the knowledge graph.

What about embedding the PDF in the knowledge graph?

That's an outstanding and forward-thinking question. You're moving beyond just structuring the extracted metadata to capturing the meaning of the entire document itself. This is a key feature of modern, AI-augmented knowledge graphs.

Yes, you can "embed" the PDF in the knowledge graph, but this term can mean two different things. Let's break them down.

Approach 1: Linking to the PDF (The "Pointer" Method)

This is the simplest interpretation. The knowledge graph contains a literal link to the original PDF file, which might be stored in a document repository or a cloud storage bucket (like Google Cloud Storage or Amazon S3).

How it works: You add a property to the HazardReport node, such as sourceURI or pdfLocation.

MERGE (r:HazardReport {reportID: "SHR-2024-08-15-001"})
SET r.sourceURI = "gs://nasa-reports/SHR-2024-08-15-001.pdf"

Pros:
- Provenance: You always have a direct link to the original, authoritative source document.
- Simplicity: It's very easy to implement.
Cons:
- No Intelligence: The graph itself understands nothing about the PDF's content. It's just a file path. All querying capabilities rely solely on the structured data you extracted.

This approach is good for archival and reference, but it's not the powerful method you're likely envisioning.

Approach 2: Embedding the PDF Content (The "Semantic" Method)

This is the state-of-the-art approach that uses vector embeddings. It involves converting the text content of the PDF into a numerical representation that captures its semantic meaning.

What is a Vector Embedding?

A vector embedding is a list of numbers (a vector) that represents a piece of text in a high-dimensional space. An AI model is trained to generate these vectors so that texts with similar meanings have vectors that are "close" to each other in that space.

Think of it as giving each document a unique coordinate in a giant "library of meaning."

How to Implement It

This process extends the ingestion pipeline we discussed earlier.

Extract Full Text: Get all the raw text from the PDF using a tool like pdfplumber.
Chunk the Text: Models have a limit on how much text they can process at once. More importantly, embedding smaller, focused chunks (e.g., a few paragraphs each) is often more effective for retrieval than embedding one massive document.

Generate Embeddings: For each text chunk, you use an embedding model to convert it into a vector.

Tools: You can use APIs like the Google Gemini API or open-source models through libraries like sentence-transformers.

Example:

# Conceptual code
from some_embedding_library import EmbeddingModel
model = EmbeddingModel('text-embedding-004') # Example model
text_chunk = "A potential buffer overflow was identified in the telemetry processing unit..."
vector = model.embed(text_chunk)
# vector is now a list of floats, e.g., [0.034, -0.582, ..., 0.911]

Store in the Knowledge Graph: You store this vector as a property on a node. You can either attach it directly to the HazardReport node or, more flexibly, create DocumentChunk nodes.
- You also need a vector index to search these embeddings efficiently. Most modern graph databases (like Neo4j, TigerGraph) now have built-in vector index capabilities.
// Create a chunk node linked to the main report MERGE (r:HazardReport {reportID: "SHR-2024-08-15-001"}) CREATE (c:DocumentChunk {text: $text_chunk, embedding: $vector}) CREATE (c)-[:CHUNK_OF]->(r)

Unlocking New Capabilities: The "Why"

This semantic approach transforms your knowledge graph from a database into a discovery engine. Here’s what you can now do:

Semantic Search: This is the killer feature. You can find reports based on concepts, not just keywords.
- Query: "Find me reports about unhandled memory allocation errors."
- Result: The system will find reports that mention "buffer overflow," "memory leak," or "segmentation fault," even if they don't use your exact search terms, because their vector embeddings are semantically similar.
Similarity and Clustering: You can instantly find the top 5 most similar hazard reports to any given report. This is invaluable for identifying recurring issues or systemic problems that aren't obvious from the structured metadata alone.
Anomaly Detection: A report whose embedding is very far from all other clusters could represent a completely novel type of hazard, flagging it for immediate human review.
Sophisticated Question-Answering (RAG): You can build a system where a user asks a natural language question. The system embeds the question, finds the most relevant text chunks from the knowledge graph using vector search, and then feeds those chunks to an LLM to generate a concise, synthesized answer with citations back to the original PDFs.

The Hybrid Model: The Best of Both Worlds

The most powerful solution combines the structured knowledge graph with semantic vector embeddings.

The Structured Graph gives you precise, explicit, and verifiable facts.
- Query: "Show all Critical hazards affecting the Flight Guidance Software."
The Vector Embeddings give you nuanced, contextual, and implicit understanding of the raw text.
- Query: "Find document chunks that are semantically similar to 'failure during orbital insertion burn'."

By combining them, you can perform incredibly powerful queries:

"Find all Critical hazards (structured query) that are semantically similar to the issue described in report SHR-123 (vector query) and return the recommended mitigations for them (structured traversal)."

In summary, embedding the PDF's meaning via vectors is a transformative step. It bridges the gap between the structured world of your ontology and the unstructured world of the source documents, creating a truly intelligent and interactive knowledge system.

From <https://gemini.google.com/app/> Google Gemini 2.5 Pro

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