#4484 Prompt to Improve KG Discovery Workflow

#4484 Prompt to Improve KG Discovery Workflow

Domain->System->Subsystem->Component->Part

each with a name and functional description

as well as cause-effect interactions as relationships

A. Get/Add Data to Knowledge Graph

1. Get 50 system domains (e.g. Mechanical, Electric, Hydraulic, ...)

2. Save system domain list to SQLite3 domains table (using Python)

3. Loop through system domain list

3.a. For each, prompt LLM to provide Python array of 25 systems

3.b. Save systems to SQLite 3 systems table, FK->domains table

4. Loop through domains/systems list

4.a. For each, prompt LLM to break down the system to subsystems, components and parts, with functional descriptions for each, as well as cause-effect interactions as relationships, as Cypher Stmts

4.b. Save Cypher statements to SQLite3 systems table

5. Loop through domains/systems list

5.a. Submit system cypher statements to knowledge graph

6. Loop through nodes in knowledge graph

6.a. For each node, prompt LLM to provide a useful desription

6.b. Embed description text in knowledge graph with relationship to node

B. Fix Knowledge Graph Inconsistencies and Missing Data

1. Prompt LLM to provide MATCH/MERGE statements to fix ...

2. Prompt LLM to provide MATCH/MERGE statements to add ...

3. Prompt LLM to provide MATCH/MERGE statements to normalize ...

C. Develop Automated Discovery Workflow

1. Search existing systems looking for opportunities to fix and improve

2. Cross-domain analysis looking for opportunities to fix and improve

3. Cross-domain analysis looking for opportunities to add systems

D. Operations

1. Run automated workflow

2. Analyze results

3. Determine follow-up

4. ...

#4484 Assess and improve the following: """Concept: Systems Engineering Knowledge Graph ..."""

This is an ambitious and highly valuable concept. Integrating systems engineering hierarchies with knowledge graphs (KGs) and LLMs is excellent for cross-domain impact analysis.

However, your current workflow has a few major architectural bottlenecks—specifically around rate limits, data redundancy, and Neo4j/Cypher generation errors. LLMs notoriously struggle with generating flawless, deeply nested Cypher statements on the first try without strict schemas.

Here is an assessment of your current plan, followed by an optimized, production-ready version of the workflow.

Architectural Assessment

The Bottlenecks

• Too Many Iterative LLM Calls (The $N^2$ Problem): In steps A.3 and A.4, looping through 50 domains $\times$ 25 systems means 1,250 separate LLM calls just for the system breakdown. This will hit API rate limits quickly and cost a fortune.

• SQL to Graph Redundancy: Storing Cypher statements inside an SQLite table (A.4.b) before executing them in Neo4j (A.5.a) creates unnecessary intermediate steps. It's cleaner to let Python parse structured data (like JSON) directly into the graph.

• The "Hallucinated Cypher" Risk: Asking an LLM to output raw Cypher statements for complex cause-effect interactions often results in syntax errors or disconnected graph fragments.

The Fixes

• Use Structured Outputs (JSON): Force the LLM to return strict JSON using Pydantic or JSON Mode. Let Python handle the Neo4j driver insertion.

• Batching: Combine steps. Generate the system, its breakdown, and its descriptions in fewer, more comprehensive prompts.

• Graph Data Science (GDS) for Discovery: In Section C, "Discovery" shouldn't just be prompt-based. You can leverage Neo4j's Graph Data Science library (like similarity algorithms or link prediction) to automatically find cross-domain opportunities.

Improved Workflow Blueprint

Here is the refined, highly scalable version of your workflow.

A. Data Ingestion & Graph Construction

Instead of generating raw Cypher, we use Python to parse structured JSON into the graph, ensuring data integrity.

[Domain] ──(HAS_SYSTEM)──> [System] ──(HAS_SUBSYSTEM)──> [Subsystem] ──(HAS_COMPONENT)──> [Part]
                                                                                            │
                                   [Element] ──(CAUSES {mechanism})──> [Element] <──────────┘

1. Define the Schema: Establish a strict Pydantic model in Python for the hierarchy (Domain -> System -> Subsystem -> Component -> Part) and the Effect relationships.

2. Seed Domains: Generate the 50 domains and store them in SQLite (good for a local cache/audit log).

3. Batch Generation: For each domain, request a batch of 5 systems fully broken down into JSON.

   Prompt Strategy: "Act as a Lead Systems Engineer. For the [Hydraulic] domain, provide 5 systems. For each system, provide its Subsystems, Components, and Parts with functional descriptions, and an array of cause-effect relationships between any of these elements."

4. Direct Ingestion: Use the Python Neo4j driver to parse the JSON and run optimized UNWIND Cypher templates to build the nodes, descriptions, and relationships simultaneously.

5. Inline Vector Embeddings: Generate the text embeddings for the functional descriptions during ingestion, saving them directly as node properties in Neo4j for vector search later.

B. Graph Verification & Enrichment (The "Cleanup" Phase)

Rather than asking the LLM to write blind MATCH/MERGE statements, extract localized subgraphs, send them to the LLM, and apply the fixes programmatically.

1. Entity Resolution: Run a Cypher query to find nodes with highly similar names (e.g., "Electric Motor" vs "Electrical Motor") using vector similarity. Prompt the LLM to resolve them into a single canonical node.

2. Relationship Validation: Scan for dangling cause-effect relationships (relationships missing a clear source or target mechanism) and use the LLM to repair or prune them.

3. Ontology Normalization: Ensure all functional descriptions follow a standardized format (e.g., "Function: [Action] | Input: [X] | Output: [Y]").

C. Automated Discovery Workflow (The Intelligence Layer)

This is where the Knowledge Graph shines over a standard SQL database.

1. Failure Mode & Effects Analysis (FMEA): * Graph Query: Find paths where a failure in a Part in the Electrical domain cascades via cause-effect chains into a Subsystem in the Hydraulic domain.

   • LLM Action: Feed this cross-domain path to the LLM to identify single points of failure and suggest redundancies.

2. Cross-Domain Analogy Search:

   • Vector Search: Compare components across different domains based on their functional description embeddings (e.g., Does a software "buffer" act similarly to a hydraulic "accumulator"?).

   • LLM Action: Identify if optimization techniques from one domain can be applied to the other.

3. Generative System Design: * Ask the LLM to identify missing links: "Based on the existing systems in the graph, what auxiliary subsystem is missing to improve thermal efficiency by 15%?"

D. Operations & CI/CD Pipeline

1. Orchestration: Use a framework like Prefect or Apache Airflow to schedule the ingestion and discovery loops.

2. Human-in-the-Loop (HITL) Dashboard: Build a simple Streamlit UI. When the automated discovery workflow finds an "opportunity to improve," it logs it as a ticket for a human engineer to approve before modifying the core system architecture.

Does this structured JSON and batching approach align better with your scaling goals, or would you prefer to dive deeper into the specific prompt engineering needed for the cause-effect relationships?

From <https://gemini.google.com/app/>  Google Gemini (3.5 Flash)