Vectors vs Graphs: Two Paradigms for AI Knowledge
Overview
Vector embeddings and knowledge graphs represent fundamentally different approaches to machine knowledge. Understanding their tradeoffs is essential for designing AI systems that know things reliably.
Vector Embeddings
What they are: Dense numerical representations where semantic similarity maps to geometric proximity. Words, sentences, or documents become points in high-dimensional space.
Strengths:
- Capture fuzzy semantic similarity automatically
- Scale well to massive corpora
- Enable retrieval without explicit structure
- Learn patterns humans don’t anticipate
Weaknesses:
- No explicit reasoning, similarity isn’t logic
- Opaque: can’t inspect why two things are “close”
- Conflate different types of relationships (antonyms may be close because they appear in similar contexts)
- No provenance or explanation
Knowledge Graphs
What they are: Explicit representations of entities, relationships, and facts as nodes and edges. Subject-predicate-object triples that can be traversed and queried.
Strengths:
- Explicit, inspectable relationships
- Support logical inference and consistency checking
- Provide provenance, every fact has a source
- Enable explanation and audit
Weaknesses:
- Require manual construction or careful extraction
- Brittle to variations in how questions are phrased
- Miss patterns not explicitly encoded
- Scale challenges for maintenance
The Hybrid Thesis
Neither approach alone is sufficient. The emerging pattern: use vectors for retrieval and relevance, use graphs for grounding, reasoning, and explanation. RAG systems increasingly combine both.
Related: 06-molecule—knowledge-graph-construction, 07-molecule—rag-core-tradeoffs, 07-organism—when-knowledge-graphs-become-memory