# Why Knowledge Graphs?

*The theoretical foundation for graph-based knowledge management*

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Before diving into queries and analytics, it helps to understand *why* knowledge graphs exist and what problems they solve.

This series builds the conceptual foundation. By the end, you'll understand:

- Why traditional retrieval fails for complex questions
- How graphs capture relationships that text cannot
- The projection pipeline that turns raw knowledge into actionable rules
- How consumers close the loop between knowledge and behavior

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## The Journey

| Part | Title | What You'll Learn |
|------|-------|-------------------|
| 1 | [The Multi-Hop Problem](part1-multi-hop-problem.md) | Why keyword search breaks down |
| 2 | [Knowledge Graphs 101](part2-knowledge-graphs.md) | Nodes, edges, and semantic structure |
| 3 | [The Projection Pipeline](part3-projection-pipeline.md) | From raw text to structured rules |
| 4 | [The Consumer Loop](part4-consumer-loop.md) | How agents use projected knowledge |
| 5 | [Pattern Completion](part5-pattern-completion.md) | Deriving new facts from structure |
| 6 | [HippoRAG First Principles](part6-hipporag.md) | Memory-inspired retrieval |

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*Ready? [Let's start with the problem](part1-multi-hop-problem.md).*
