How Metadata, Graphs, Maps, and Stories Build Provenance Ecosystems in the GenAI Semantic Web

As I build MyFeeds.AI and the Cyber Boardroom, I'm having the same problem that everybody that is doing GenAI workflows and agents is facing: GenAI systems need better data structures to deliver truly meaningful, contextual insights.

Basically raw text is just not good enough as input and outputs

We've all seen the limitations of large language models when they lack proper context or provenance for the information they're processing. We need metadata, graphs and maps, so that we can create meaningful stories, narratives, decisions and insights with verifiable origins.

This challenge has led me deep into the world of Semantic Knowledge Graphs, which is now the backbone of how I'm structuring information in order to create provenance ecosystems that enable personalised security intelligence.

The reality is that GenAI's effectiveness and trustworthiness depends heavily on the ability to trace, verify, and contextualise information throughout its lifecycle.

In this article, I'll expand on three progressively powerful artefacts that enhance our ability to build comprehensive provenance ecosystems for GenAI: metadata, graphs, and maps.

In my current focus, it is these interconnected elements that ultimately enable the creating of personalised stories with clear lineage that truly resonate with specific audiences, which is the core value proposition behind both MyFeeds.ai and The Cyber Boardroom.

Metadata: The Foundation Layer

At its most basic level, metadata provides essential context through name-value pairs, who are attributes that describe content.

For example, these include:

• Author
• Title
• Source
• Timestamp
• Content hash

These primitive elements are fundamentally important because they immediately reduce uncertainty. Knowing who created a document, where it was published, and when, creates a basic framework of trust. Even technical elements like content hashes provide critical verification capabilities.

However, metadata alone has a significant limitation: it cannot resolve ambiguity.

If metadata indicates an author named "John Smith" - which John Smith? If it references "London" - is that London, UK or London, Ontario? For each metadata element, we need ways to disambiguate and connect the dots. This is where metadata reaches its limits and we need something more powerful.

Graphs: Creating Connections

The next evolution beyond simple metadata is the graph - composed of nodes and edges. In this structure:

• Nodes represent entities (essentially metadata points)
• Edges represent the connections between these entities

This structure allows us to "follow the rabbit holes" and establish clear relationships. We can now distinguish between London, UK and London, Ontario because each exists within a network of relationships to other entities (countries, regions, etc.).

Graphs enable us to build context around isolated data points. They transform disconnected metadata into an interconnected web where meaning emerges from relationships.

But to truly maximise the value of graphs, we need to add another layer of sophistication.

Semantic Knowledge Graphs: Adding Meaning

A semantic knowledge graph elevates basic graph structures by incorporating:

1. Ontologies - frameworks that define the rules and classifications of entities
2. Taxonomies - hierarchical relationships between those entities

Ontologies tell us that if we're talking about a person, we know it's a human being with certain characteristics. If we're discussing a city, we know it exists within a specific geographical and political context.

Taxonomies create the valuable hierarchical connections - a city belongs to a country, which belongs to a continent, which exists on a planet. These structured relationships eliminate ambiguity and create clear pathways of understanding.

What makes semantic knowledge graphs particularly powerful is that they should exist within a web of trust. Not all sources have equal reliability, and the system should reflect this reality. Sources earn trust through consistency and accuracy, allowing users to make informed judgments about the information they encounter.

Maps: Adding Position and Movement

While graphs significantly enhance our understanding compared to metadata alone, they still have limitations - particularly when it comes to visualisation and conveying information effectively.

This is where maps enter the picture. A map is fundamentally built on graph structures (nodes and edges), but with crucial additional elements:

• Position has meaning
• Movement and direction are represented
• Attributes create a visual language
• Landscapes and patterns become visible

Unlike graphs, where the position of nodes doesn't affect meaning, in maps the exact positioning matters significantly. Each entity exists in relation to others in a defined space, creating additional layers of meaning.

In geographical maps, this manifests as latitude and longitude. In concept maps like Wardley Maps, position might represent evolution from genesis to commodity. Regardless of the specific implementation, this spatial dimension adds crucial context.

The power of maps lies in their ability to make patterns visible that would remain hidden in graph format. They create what military strategists call "situational awareness", a comprehensive understanding of the landscape and how elements relate to each other.

Stories: Communicating with Context

The ultimate purpose of all these tools - from metadata to maps - is to enable effective communication. Maps are particularly powerful because they allow multiple people to look at the same representation and have meaningful discussions based on shared understanding.

This shared understanding enables storytelling - perhaps the most powerful communication tool humans have created. When built upon the rich foundation of semantic knowledge graphs and maps, these stories gain unprecedented depth and relevance.

In my work with MyFeeds.AI, this progression from metadata to stories is exactly what we're implementing. We use semantic knowledge graphs to connect information across multiple dimensions, creating rich representations that allow us to generate highly personalized content.

The Critical Role of Personas

The final element in this progression is understanding who we're communicating with. Effective communication must account for:

• Language preferences
• Cultural context
• Professional role
• Specific interests and needs

By connecting persona graphs with content graphs, we can create truly customised communications - whether that's a single paragraph, a comprehensive report, or an interactive experience.

The length doesn't matter. What matters is that the communication is crafted specifically for its intended audience, leveraging all the contextual understanding made possible by our progression from metadata to graphs to maps.

Putting It All Together: The GenAI Connection

The journey from basic metadata to rich, personalised storytelling represents a fundamental evolution in how we deploy GenAI effectively:

1. Metadata provides essential attributes but lacks contextual connections
2. Graphs create relationships between entities, resolving ambiguities
3. Semantic knowledge graphs add meaning through ontologies and taxonomies
4. Maps incorporate position and movement, creating visual understanding
5. Stories leverage these tools to communicate effectively with specific audiences

When GenAI has access to properly structured, contextually rich information through semantic knowledge graphs, it transforms from a generic language model to a precision tool for delivering personalised intelligence.

At MyFeeds.AI, I'm seeing this difference firsthand. By using semantic structures rather than raw text, it is possible to create of truly personalised security intelligence that maintains provenance, reduces hallucinations, and delivers actionable insights tailored to specific audiences.