Deep Dive into the world of ODPs - Knowledge Graphs

# Knowledge Graphs: The Fundamentals

What they are:

Core components:

- Nodes (Entities): Individual data points - people, products, concepts, events

- Edges (Relationships): How those entities connect - "works for," "purchased by," "related to"

- Semantic metadata: Context that gives meaning to the connections

Think of it like this: A database says "Carolyn is a Principal Enterprise Architect." A knowledge graph says "Carolyn [IS_A] Principal Enterprise Architect [WORKS_AT] home [INTERESTED_IN] Knowledge Graphs [RELATES_TO] Data

Architecture."

Why Enterprise Architects Care

Knowledge graphs are particularly relevant to your role because they:

1. Break down data silos - Connect fragmented enterprise data across departments

2. Enable smarter AI - Provide context for AI systems (crucial for agentic AI in 2026)

3. Support data fabric architectures - Create a semantic layer for unified data views

4. Improve enterprise search - Return contextually relevant results, not just keyword matches

Real enterprise use cases:

- Customer 360 views (merging CRM, support, purchase history)

- Incident triage (connecting alerts, code changes, and team ownership)

- Compliance mapping (linking regulations to systems and processes)

- Smart manufacturing (optimizing production through connected operational data)

Good Starting Resources

Comprehensive introductions:

- https://www.ontotext.com/knowledgehub/fundamentals/what-is-a-knowledge-graph/ - Solid foundation

- https://textmine.com/post/an-introduction-to-knowledge-graphs - Practical overview

- https://arxiv.org/abs/2003.02320 - Academic deep dive if you want rigor

Enterprise-focused:

- https://www.puppygraph.com/blog/enterprise-knowledge-graph - Your role specifically

- https://enterprise-knowledge.com/top-graph-use-cases-and-enterprise-applications-with-real-world-examples/related/ - Real-world examples

- https://www.techtarget.com/searchdatamanagement/tip/5-knowledge-graph-use-cases-in-data-fabric-architecture - Architecture perspective

2026 context:

- https://www.puppygraph.com/blog/knowledge-graph-examples - Current state of the field

- https://www.superblocks.com/blog/enterprise-knowledge-graph - Why they matter now

Your Next Steps

I'd suggest:

1. Start with the Ontotext Fundamentals article - it's concise and well-structured

2. Move to the Enterprise Knowledge Graph piece to see how this applies to your work

3. Skim one of the real-world use cases articles to cement the concepts

Spend your 45-50 minutes exploring these, and when you're done, come back and tell me what clicked (or what confused you). Then we'll move to Task #2 on nodes and edges.

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Sources:

- https://www.ontotext.com/knowledgehub/fundamentals/what-is-a-knowledge-graph/

- https://www.puppygraph.com/blog/knowledge-graph-examples

- https://searchengineland.com/knowledge-graphs-introduction-448128

- https://arxiv.org/abs/2003.02320

- https://en.wikipedia.org/wiki/Knowledge_graph

- https://textmine.com/post/an-introduction-to-knowledge-graphs

- https://www.puppygraph.com/blog/enterprise-knowledge-graph

- https://enterprise-knowledge.com/top-graph-use-cases-and-enterprise-applications-with-real-world-examples/related/

- https://www.techtarget.com/searchdatamanagement/tip/5-knowledge-graph-use-cases-in-data-fabric-architecture

- https://www.glean.com/blog/knowledge-graph-agentic-engine

The universe may be indifferent to your learning journey, but at least you'll understand how to model its indifference as a graph structure.