#3555 Google Gemini 2.5 Pro: Developing then Merging 3 Knowledge Graphs under One M2M KG

#3555 Google Gemini 2.5 Pro: Developing then Merging 3 Knowledge Graphs under One M2M KG

1. Given that I have 3 separate knowledge graphs, one for NASA Artemis Program, Gateway Program, and Extravehicular Activity & Human Surface Mobility (EHP) Program, how can I merge these knowledge graphs within a encompassing Moon to Mars Program knowledge graph?
2. Seems like a better idea is to design the 3 (or more) separate knowledge graphs with the same definition critria so that they can be built separately and independently, yet being easier to merge when the decision is made
3. Once merged, the combined knowledge graph should support cross-program knowledge sharing
4. How could this common set of definition criteria be used to standardize knowledge graphs for all NASA programs, existing and future?
5. What are common needs for the NASA programs that knowledge graphs can support?
6. What about operation mission needs like contingency planning and response?

#3555 Given that I have 3 separate knowledge graphs, one for NASA Artemis Program, Gateway Program, and Extravehicular Activity & Human Surface Mobility (EHP) Program, how can I merge these knowledge graphs within a encompassing Moon to Mars Program knowledge graph?

You can merge your three separate knowledge graphs (KGs) into a comprehensive Moon to Mars Program KG by following a structured, multi-step process focused on aligning their structure and linking their common entities. The core idea is to create a unified model and then combine the data, resolving any duplicates or conflicts along the way.

Here’s a breakdown of the process:

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1. Schema & Ontology Alignment 🗺️

Before you merge the data, you must first merge the rules and structure of the graphs. This is called schema or ontology alignment. Your goal is to create a single, unified "Moon to Mars" ontology that all three source graphs can map to.

• Define a Unified Model: Create a target ontology for the final Moon to Mars KG. This will include all the important classes (like Mission, Astronaut, Vehicle, Hardware) and properties (like hasCrew, docksWith, manufacturedBy).

• Map Classes and Properties: Identify equivalent concepts across your three KGs and map them to your unified model. For instance:

  • The class artemis:CrewMember in the Artemis KG might be equivalent to gateway:Astronaut in the Gateway KG. You can formally state this equivalence using a mapping like artemis:CrewMember owl:equivalentClass gateway:Astronaut.

  • The relationship ehp:hasSuitComponent might be a sub-property of a more general m2m:hasComponent in your new model. You'd map this using ehp:hasSuitComponent rdfs:subPropertyOf m2m:hasComponent.

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2. Entity Resolution (Instance Matching) 🧑‍🚀

Once the schemas are aligned, you need to find nodes across the different graphs that refer to the same real-world thing. This process is known as entity resolution or instance matching.

For example:

• The Artemis KG might have a node for "Orion Crew Vehicle".

• The Gateway KG might have a node called "Orion".

• The EHP KG might mention "Orion" in a property describing a piece of equipment.

You need to identify that these all refer to the same spacecraft. Techniques for this include:

• Identifier Matching: The easiest way is matching on unique identifiers or URIs if they exist.

• Property-Based Matching: Find entities that share key properties. If two Vehicle nodes share the same manufacturer, launch date, and mission name, they are almost certainly the same entity.

• String Similarity: Use fuzzy string matching algorithms to compare names like "xEMU" and "Exploration Extravehicular Mobility Unit".

• Link-Based Matching: If two entities are connected to many of the same other entities (e.g., both are linked to the Artemis II mission and NASA), they are likely the same.

When you find a match, you merge these nodes into a single canonical node in your new Moon to Mars KG, combining all their properties and relationships.

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3. Graph Merging Techniques ⚙️

With your alignment rules and entity matches defined, you can perform the actual merge. There are two primary approaches:

Physical Merge (ETL Process)

This is the most common method. You physically combine the data into a new, single knowledge graph.

1. Extract: Pull the triples (subject-predicate-object) from each of the three source KGs.

2. Transform: Apply your schema mappings and entity resolution rules. This involves renaming properties, merging nodes, and cleaning the data to conform to your unified Moon to Mars ontology.

3. Load: Insert the transformed, clean triples into your new, final knowledge graph.

Tools like Neo4j, GraphDB, or custom scripts using libraries like RDFLib (Python) or Apache Jena (Java) are perfect for this ETL-style process.

Federated Query

Alternatively, you can leave the three KGs separate and query them as if they were one. This is called federation.

Using a query language like SPARQL, the SERVICE clause lets you send a sub-query to another knowledge graph. This approach is less permanent and is useful when you can't or don't want to physically merge the databases. However, it can be slower and more complex to manage.

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4. Post-Merge Refinement & Validation ✅

After the merge, your work isn't quite done. You should:

• Validate: Use a validation language like SHACL or SHEX to ensure your new, merged graph conforms to the rules of your unified Moon to Mars ontology.

• Deduplicate: Run queries to find any duplicate entities or relationships that your process might have missed.

• Infer New Links: Use a reasoner to infer new knowledge from the combined data. For example, if an Astronaut is part of an Artemis Mission which docksWith the Gateway, you can infer that the Astronaut has been to the Gateway.

Seems like a better idea is to design the 3 (or more) separate knowledge graphs with the same definition critria so that they can be built separately and independently, yet being easier to merge when the decision is made

Yes, you're absolutely right. That is a far superior and more strategic approach.

Designing separate knowledge graphs with a common, predefined set of rules from the start is a best practice in knowledge management. This strategy is known as using a shared or common ontology.

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The Shared Ontology Approach 🏗️

Instead of creating three different schemas and later trying to align them, you would first create a single, authoritative "Moon to Mars" ontology. This ontology serves as a master blueprint for any KG built within the program.

It would define the core entities and relationships for everyone to use:

• Classes: Mission, Spacecraft, Astronaut, HardwareComponent, ScientificPayload, Organization

• Properties: hasCrew, docksWith, manufacturedBy, launchDate, hasTarget

Each team (Artemis, Gateway, EHP) would then build its specific KG using this shared rulebook.

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Key Advantages of This Method

• Drastically Simplified Merging: The merge process transforms from a complex transformation and mapping project into a simple data aggregation. You can essentially union the graphs together because the nodes and relationships already mean the same thing. artemis:Orion and gateway:Orion would already be defined using the same class and properties, making them easy to identify and merge.

• Guaranteed Consistency: Data quality and consistency are enforced from the beginning. You eliminate ambiguity because everyone is using the same governed vocabulary.

• Enhanced Interoperability: The knowledge graphs are "born" able to communicate with each other. Federated queries become trivial to write because you don't need complex mapping logic within the query itself.

• Scalability: As the Moon to Mars program expands to include new projects (e.g., a "Mars Surface Operations" KG), they can easily be built using the same ontology and integrated into the ecosystem.

In short, your proposed "design for merge" strategy proactively solves the biggest challenges of knowledge graph integration before they even become problems.

Once merged, the combined knowledge graph should support cross-program knowledge sharing

Of course. Supporting cross-program knowledge sharing is the primary reason for merging the knowledge graphs. The unified graph achieves this by creating a single, interconnected network where relationships can be traced across the original program boundaries.

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How the Merged Graph Enables Knowledge Sharing 🔗

The merged "Moon to Mars" knowledge graph acts as a single source of truth, breaking down the data silos that existed when the information was separate. This enables powerful new queries that were previously impossible.

The key is that entities common to multiple programs now act as bridges connecting different domains.

• Shared Entities: An entity like the Orion Spacecraft is central to the Artemis program, delivers crew who will perform EHP activities, and docks with the Gateway. In the merged graph, this single Orion node links all three domains.

• Path Traversal: You can now ask complex questions by traversing these bridges. For example:

  1. Start with a piece of EHP hardware: "Which astronauts are trained to use the xEMU spacesuit?"

  2. Cross into the Artemis domain: "Which Artemis missions are those astronauts assigned to?"

  3. Cross into the Gateway domain: "Will any of those missions involve docking with the Gateway to test the new airlock?"

This ability to follow connections seamlessly from a spacesuit (EHP) to a mission (Artemis) to an orbital station (Gateway) is the essence of cross-program knowledge sharing. It allows planners, engineers, and scientists to see the cascading impacts and dependencies of any component, mission, or decision across the entire Moon to Mars enterprise.

How could this common set of definition criteria be used to standardize knowledge graphs for all NASA programs, existing and future?

Excellent question. Applying this common definition criteria, or shared ontology, across all NASA programs is the key to unlocking an agency-wide, interconnected data ecosystem. This would create a NASA Enterprise Knowledge Graph (EKG), a strategic asset for discovery and decision-making.

Here is how this standardization could be implemented and what it would enable.

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The Strategy: A Tiered Ontology Architecture

A single, monolithic ontology for all of NASA would be too massive and rigid. The solution is a tiered, or layered, architectural approach.

Tier 1: The NASA Core Ontology (The Trunk)

This is the foundational layer. NASA would establish a governance body—a "Knowledge Council" or "Center for Knowledge Graph Excellence"—to define and maintain a high-level enterprise ontology. It would only contain concepts and relationships that are truly universal across the entire agency.

• Core Classes: Mission, Project, Personnel, Spacecraft, Facility, Organization, Technology, ScientificDataProduct.

• Core Properties: hasObjective, ledBy, launchedOn, operatedBy, hasComponent.

This core ontology would be stable, changing infrequently, and would serve as the common language for all programs.

Tier 2: Directorate/Domain-Level Ontologies (The Branches)

Each major NASA directorate or program type would extend the Core Ontology with concepts specific to its domain.

• Human Exploration (M2M Program): Extends the core with classes like GatewayModule, LunarLander, SpacesuitSystem, and properties like docksWith.

• Planetary Science: Extends with Rover, Orbiter, CelestialBody, GeologicalSample, and properties like hasTargetPlanet.

• Earth Science: Extends with Satellite, EarthSensor, ClimateModel, Measurement, and properties like monitorsPhenomenon.

• Aeronautics: Extends with ExperimentalAircraft, HypersonicTest, Airfoil, and properties like hasDragCoefficient.

Crucially, a Rover in the Planetary Science ontology is still a type of Spacecraft from the Core Ontology, ensuring interoperability.

Tier 3: Project-Specific Vocabularies (The Leaves)

Individual projects, like a specific instrument on the Mars rover, could further extend the domain ontology for their highly detailed internal needs (e.g., SpectrometerDetector, LaserFocusLens).

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Implementation and Governance

1. Establish a Governance Body: A dedicated team is essential for managing the Core Ontology, reviewing proposed extensions from programs, and ensuring consistency.

2. Mandate for Future Programs: NASA would make the use of this tiered ontology architecture a standard requirement for all new missions and projects.

3. Provide Tooling and Training: To ensure adoption, the governance body would provide standard tools, software libraries, and training to make it easy for programs to create and manage their KGs according to the standard.

4. Strategy for Existing/Legacy Data: For existing programs and historical data (e.g., Apollo, Shuttle programs), a gradual "retrofitting" process would map their existing data models onto the new standard, allowing historical knowledge to be integrated over time.

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Transformative Benefits for NASA

By standardizing in this way, NASA would enable:

• Agency-Wide Search and Discovery: A researcher could ask, "Show me all Technologies related to radiation hardening developed for the Artemis Program that could be applied to a future mission to Europa." This query seamlessly crosses Human Exploration and Planetary Science domains.

• Breaking Down Directorate Silos: A materials science breakthrough in Aeronautics could be automatically linked to spacecraft development in Exploration Systems if both use the same core concept of Material.

• AI and Advanced Analytics: A standardized EKG is the perfect foundation for training powerful AI models. You could build systems to predict mission risks, identify underutilized technologies, or find subject matter experts anywhere in the agency.

• Historical Analysis: You could trace the lineage of a technology or contractor from the Apollo era, through the Shuttle program, to the suppliers for Artemis today.

Adopting a common set of definition criteria isn't just a data management upgrade; it's a strategic investment in making NASA's collective knowledge a discoverable, intelligent, and enduring asset for generations to come.

What are common needs for the NASA programs that knowledge graphs can support?

Across NASA's diverse and ambitious programs, from the Artemis missions to the James Webb Space Telescope, there are several persistent, fundamental needs that knowledge graphs are uniquely positioned to address. These challenges often revolve around complexity, scale, and the long lifecycles of projects.

Here are the common needs for NASA programs that knowledge graphs can support:

1. Integrating Disparate and Siloed Information

The Need: NASA programs generate a massive amount of heterogeneous data (engineering designs, test results, scientific papers, mission plans, project management documents) stored in different formats and systems. A systems engineer may need to connect a requirement in a DOORS database to a specific part in a CAD model and a test result in a PDF report.

How Knowledge Graphs Help:

• Unified View: A knowledge graph can ingest and link these disparate sources without forcing them into a rigid, tabular structure. It creates a single, queryable network of all program information.

• Example: A user could ask, "Show me all components made from 'Alloy X' that have failed thermal-vacuum tests and are linked to 'Requirement 4.5.2' for the Orion capsule." This query traverses links between materials science databases, test reports, and requirements management systems.

2. Managing Complex System-of-Systems Interactions

The Need: Modern missions are not single spacecraft but intricate "systems of systems." The Artemis program, for instance, involves the Space Launch System (SLS), the Orion spacecraft, the Gateway station, Human Landing Systems (HLS), and new spacesuits. A change in one system can have cascading impacts on all the others.

How Knowledge Graphs Help:

• Interface Management: KGs can explicitly model the physical, logical, and data interfaces between systems. This allows for automated impact analysis.

• Example: If the Gateway's power output specification is updated, a knowledge graph can instantly identify all systems and components that depend on that interface (e.g., docked science experiments, Orion's charging system) and flag them for review.

3. Requirements Traceability, Verification, and Validation

The Need: Every NASA program is built on a complex hierarchy of requirements. It is a monumental task to trace a high-level scientific goal (e.g., "Search for signs of past life on Mars") down to a specific technical requirement on a rover's drill bit and then to the verification test that proves it works.

How Knowledge Graphs Help:

• End-to-End Lineage: KGs create a clear, traversable path from a top-level requirement to its derived sub-requirements, the system components that satisfy them, and the tests that validate them.

• Example: Program managers can ask, "Which requirements for the Europa Clipper mission are not yet verified by a successful test?" The graph can highlight gaps in the verification process, which is critical for pre-launch readiness reviews.

4. Knowledge Retention and Expertise Discovery

The Need: NASA's programs often span decades, leading to workforce transitions and retirements. Critical "know-how" and undocumented knowledge can be lost when an expert leaves. Furthermore, in an organization as large as NASA, finding the right person with a specific, niche expertise is a major challenge.

How Knowledge Graphs Help:

• Connecting People to Data: KGs can link individuals to the documents they've authored, the projects they've worked on, the systems they've designed, and the skills they possess. This creates an "organizational memory."

• Expert Finders: By modeling these connections, the graph becomes a powerful expert locator.

• Example: A new engineer facing a propulsion issue on a satellite could query, "Who at NASA has experience with cold-gas thrusters used in missions beyond Earth's orbit?" The graph could point them to veteran engineers, relevant technical reports, and lessons-learned documents from past missions. NASA is actively building a "People Graph" to address this very need.

5. Supply Chain and Risk Management

The Need: NASA relies on a vast network of external contractors and suppliers. Understanding the full supply chain—and the risks associated with it—is vital. A single-source supplier for a critical component represents a significant risk to the schedule and budget.

How Knowledge Graphs Help:

• Mapping Dependencies: KGs can model the entire supply chain, linking components to their suppliers, sub-suppliers, materials, and country of origin.

• Risk Analysis: By analyzing these connections, managers can identify single points of failure and other vulnerabilities.

• Example: A query like, "Which components on the SLS rocket are dependent on Supplier Y, and what other NASA projects would be affected if that supplier fails to deliver?" can proactively identify and help mitigate program-level risks.

What about operation mission needs like contingency planning and response?

Of course. Operational needs like contingency planning and response are a critical area where knowledge graphs offer immense value, especially in the high-stakes environment of a live mission.

Here’s how a knowledge graph can support those specific needs, moving from the planning phase to a real-time response scenario in Mission Control.

1. Proactive Contingency Planning (Before and During the Mission)

Before an anomaly ever occurs, the knowledge graph can be used to build more robust and comprehensive contingency plans.

• Automated Failure Mode and Effects Analysis (FMEA): Traditionally, engineers spend thousands of hours trying to map out what might happen if a component fails. A knowledge graph can automate much of this. By modeling the connections between all systems (power, data, thermal, structural), you can run queries that simulate a failure.

  • Example Query: "If 'Coolant Pump A' fails, show all downstream components that will lose thermal regulation and the expected time until they exceed their operational temperature limits." This allows the team to identify unforeseen cascading failures and write procedures for them ahead of time.

• Linking Procedures to Faults: The graph can formally link every known fault signature or alarm code directly to its corresponding contingency procedure document. This creates an intelligent map where a specific problem is already tied to its pre-approved solution.

2. Real-time Anomaly Response (When Seconds Count)

Imagine an unexpected alarm appears on a flight controller's screen at the Johnson Space Center here in the Houston area. This is where the knowledge graph transitions from a planning tool to an indispensable crisis-response assistant.

Let's walk through a hypothetical scenario: An unexpected pressure drop is detected in a helium tank for the Human Landing System (HLS).

Step 1: Rapid Diagnosis (What is the root cause?) The flight controller doesn't just see a fault code; they query the knowledge graph. Instead of manually searching through hundreds of pages of schematics, the graph instantly displays all possible upstream causes for the pressure drop.

• KG Response: The graph highlights a known leaky valve type, a faulty sensor, and a potential structural weakness identified in pre-flight testing. It shows that the Helium Tank is connected to Valve-7B, which is connected to a specific Regulator, providing an immediate diagnostic path.

Step 2: Instant Access to the Correct Procedure The graph doesn't just suggest the problem; it provides the solution.

• KG Response: The Valve-7B node is directly linked to Contingency Procedure HLS-EP-21A: "Isolate Helium Tank B Manifold." The flight controller has the correct procedure on their screen in seconds, not minutes.

Step 3: Real-Time Impact Assessment (What are the consequences?) As the crew is actioning the procedure, the flight director needs to know the ripple effects.

• KG-Powered Queries:

  • "With Helium Tank B isolated, what is the remaining engine burn time available?"

  • "Which future landing site options are now unreachable due to the reduced fuel budget?"

  • "Are any science payloads affected by this new operational constraint?"

The graph answers these questions instantly by traversing the connections between the helium system, the propulsion system, mission trajectory plans, and science payload manifests.

Step 4: Connecting to the Right Human Expert The situation may require input from the engineer who knows the system best.

• KG Response: The controller queries for the expert linked to Valve-7B. The graph identifies the lead propulsion engineer, displays their contact information, and confirms they are the designated on-call expert for this system during this phase of the mission.

In essence, the knowledge graph acts as a cognitive assistant for Mission Control. It augments human expertise by managing the overwhelming complexity of the spacecraft, allowing the team to focus on decision-making rather than information retrieval. It's the tool that could help solve the next "fit a square peg in a round hole" problem, but with the power of connected data.

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