“Ontology,” “knowledge graph,” “context graph,” “information graph,” “vector database” — these terms get used interchangeably, and they are not interchangeable. Each is a different way to structure meaning, each answers a different question, and AI agents need different ones for different jobs. This guide pins down what each actually is, shows the same dataset modeled six ways so you can see the difference, and explains which layer matters for RAG, GraphRAG, semantic search, AEO and GEO.
The one-line version
A taxonomy files things. An ontology defines what can exist. A knowledge graph records what's true. An information graph maps your content to that truth and to demand. A context graph decides what's relevant right now. A vector index / vector database finds what's similar. They stack — and modern AI agents use all of them.
New to this? Read the plain-English on-ramp
If you've used a spreadsheet (rows and columns) and a folder tree on your computer (nested folders), you already understand two of the six structures. Here's the same idea for the other four, in the bluntest words possible:
- —Taxonomy = a folder tree. “Footwear → Running → Road → Nike Pegasus 41.” One parent each. Great for navigation; terrible at expressing “who made this” or “what's this for.”
- —Ontology = the schema / rulebook. Lists what types of things can exist (Product, Brand, Activity), how they're allowed to relate (
Product hasBrand Brand), and what's not allowed. No actual data yet — just the empty form, like a spreadsheet's column headers before any rows are filled in. - —Knowledge graph = the rulebook filled in with real things, then linked to the rest of the web. “Nike Pegasus 41 — madeBy → Nike — sameAs → Wikidata's Nike.” This is what Google, Bing, ChatGPT and Perplexity reason over when they answer factual questions.
- —Information graph = your content map. “The page
/guides/road-shoesmentions Pegasus 41 and targets the query best road running shoes.” It's the SEO/AEO layer that connects your URLs to entities and to demand. - —Context graph = the personalization layer. Same knowledge graph + who's asking + when + where + what's fresh and trusted. It decides which of two correct answers to surface for this person right now.
- —Vector index / vector database = similarity by vibes. Every piece of content is turned into a list of ~1,500 numbers (an embedding). Search means finding the lists most similar to your query's list. No
WHERE clauses, no edges — just nearness in meaning-space. Pinecone, Weaviate, Qdrant, and pgvector all do this. It's how RAG retrieves candidate text fast.
If you only remember one thing
Graphs = explicit truth. Vectors = fuzzy similarity. Modern AI systems use both — vectors to find candidate content (recall) and a knowledge graph to verify the facts (precision). The combined pattern has a name: GraphRAG (Microsoft Research, 2024).
See it: one domain, six structures
Everything below uses a single, deliberately small domain — a running-shoe retailer with a content site — so the structures are directly comparable. Watch how the same information changes shape depending on what you're trying to do with it. (Hover any node to trace its connections — and hit Explore in 3D on any figure to orbit it in space.)
Read the stack from the bottom up. Each layer is enabled by the one beneath it; the numbers (01–06) match the six sections that follow. The vector index sits to the side because it runs in parallel — different math, same dataset.
Each layer rests on the one below: the ontology gives the knowledge graph its grammar, the knowledge graph gives the information and context graphs their facts, and the vector index runs alongside as a parallel, schema-free retrieval substrate. Production AI agents query multiple layers per request — not one. Now let's define each one precisely.
1. Taxonomy — the filing system
Taxonomy. A strict tree. Every node has exactly one parent and the only relationship is “is-a”. Great for navigation; blind to any link that isn’t hierarchy.
Graph data (text) — 7 entities, 6 relationships
- Footwear —is-a→ Running Shoes
- Running Shoes —is-a→ Road Running
- Running Shoes —is-a→ Trail Running
- Road Running —is-a→ Nike Pegasus 41
- Road Running —is-a→ Hoka Clifton 9
- Trail Running —is-a→ Hoka Speedgoat 6
Taxonomy
aka Hierarchy · Classification treeA taxonomy is a strict tree that classifies things into nested parent–child categories using a single relationship: “is a kind of.”
A taxonomy organizes a domain into levels. Each item has exactly one parent, and the only edge that exists is hierarchy. It is the simplest way to impose order, which is why site navigation, product categories, and library systems are taxonomies. Its strength — one clean path to everything — is also its ceiling: it cannot express that a product is made by a brand or suited for an activity, because those are not parent–child links.
- Analogy
- A filing cabinet. Every document lives in exactly one folder, and folders nest inside drawers. You always know where something goes, but a document can only be in one place at a time.
- Example
- Footwear → Running Shoes → Road Running → Nike Pegasus 41. The Pegasus is filed under Road Running and nowhere else.
- Role for AI agents
- Taxonomies give agents a reliable scaffold for browsing and disambiguation, but on their own they carry almost no meaning an agent can reason with. They are the skeleton, not the brain.
2. Ontology — the blueprint
Ontology. The blueprint: the classes that exist, the relationship *types* allowed between them, and the rules. It defines meaning — not data. Reusable across every product you ever add.
Graph data (text) — 6 entities, 5 relationships
- Product —hasBrand→ Brand
- Product —belongsTo→ Category
- Product —suitedFor→ Activity
- Product —hasAttribute→ Attribute
- Product —constrained-by→ Rule
Ontology
aka Schema · T-Box · VocabularyAn ontology is the formal blueprint of a domain: the classes that can exist, the types of relationships allowed between them, and the rules that govern them — independent of any actual data.
Where a taxonomy only nests categories, an ontology defines the full grammar of a domain. It says a Product can have a Brand, belong to a Category, be suited for an Activity, and carry Attributes — and it can enforce rules such as “every Product must have exactly one Brand.” Schema.org is a widely-cited, web-scale vocabulary that functions as a lightweight ontology in practice. Formally, the ontology layer (the “T-Box” in Description Logic) defines classes, relationship types, and rules; instances live in the “A-Box.” OWL ontologies can include named individuals, but in practice we keep the two layers separate for clarity.
- Analogy
- The architectural blueprint and building code for a house. It specifies what rooms and connections are permitted and the rules they must obey, before a single brick is laid.
- Example
- Defining that the class Product relates to Brand via
hasBrand, to Activity viasuitedFor, and thatcushioningmust be one of {minimal, moderate, max} — for every shoe you will ever add. - Role for AI agents
- The ontology is what lets an agent interpret data consistently. Shared vocabulary plus rules means two systems can exchange facts without misreading them, and a reasoner can infer new ones. It is the contract that makes a knowledge graph trustworthy.
import Graph from "graphology";
// An ontology models *types*, not data.
const ontology = new Graph({ type: "directed" });
["Product", "Brand", "Category", "Activity", "Attribute"]
.forEach((cls) => ontology.addNode(cls, { kind: "class" }));
// Allowed relationship TYPES between classes
ontology.addEdge("Product", "Brand", { rel: "hasBrand" });
ontology.addEdge("Product", "Category", { rel: "belongsTo" });
ontology.addEdge("Product", "Activity", { rel: "suitedFor" });
ontology.addEdge("Product", "Attribute",{ rel: "hasAttribute" });
// A rule the data must obey
ontology.setNodeAttribute("Product", "constraint",
"hasBrand exactly 1 Brand");3. Knowledge Graph — the facts
Knowledge Graph. The ontology, populated with real facts. Named entities and typed, directed edges — machine-readable and resolvable to the wider web via sameAs. This is what AI answer engines reason over.
Graph data (text) — 8 entities, 7 relationships
- Nike Pegasus 41 —madeBy→ Nike
- Hoka Clifton 9 —madeBy→ Hoka
- Nike Pegasus 41 —suitedFor→ Road Running
- Hoka Clifton 9 —suitedFor→ Road Running
- Nike Pegasus 41 —has→ Moderate Cushioning
- Hoka Clifton 9 —has→ Max Cushioning
- Nike —sameAs→ Wikidata: Nike
Knowledge Graph
aka Entity graph · A-Box · Fact graphA knowledge graph is a network of real-world entities and the typed, directed relationships between them — the ontology populated with actual facts and linked to the wider web.
A knowledge graph turns the ontology's empty form into a living web of facts: named entities (Nike Pegasus 41, Nike, Road Running) connected by meaningful edges (madeBy, suitedFor). Crucially, entities resolve — a sameAs link to Wikidata or Google's Knowledge Graph anchors your “Nike” to the canonical, global one. This is the layer Google, Bing, and AI answer engines reason over to understand who you are, what you offer, and how it all connects.
- Analogy
- A subway map of facts. Stations are entities and the colored lines are typed relationships; you can trace a path from any fact to any related fact.
- Example
- Nike Pegasus 41 —madeBy→ Nike; —suitedFor→ Road Running; —has→ Moderate Cushioning. Nike —sameAs→ Wikidata:Nike.
- Role for AI agents
- Knowledge graphs give agents precise, verifiable, traversable facts. When an answer engine cites a specific spec or relationship, it is reading a graph, not guessing from text. This is the backbone of factual grounding and entity-based SEO.
const kg = new Graph({ type: "directed" });
// Instances (the A-Box) that obey the ontology above
kg.addNode("pegasus", { type: "Product", label: "Nike Pegasus 41" });
kg.addNode("nike", { type: "Brand", label: "Nike" });
kg.addNode("road", { type: "Activity", label: "Road Running" });
// External entity — declared before the sameAs edge can reference it.
kg.addNode("wikidata:Q483915", { type: "External", label: "Wikidata: Nike" });
kg.addEdge("pegasus", "nike", { rel: "madeBy" });
kg.addEdge("pegasus", "road", { rel: "suitedFor" });
kg.addEdge("nike", "wikidata:Q483915", { rel: "sameAs" });
// Traversal answers a precise question, with a verifiable path:
// "What road-running shoes does Nike make?"
const nikesRoadShoes = kg.filterNodes((n, attr) =>
attr.type === "Product" &&
kg.outNeighbors(n).includes("nike") &&
kg.outNeighbors(n).includes("road"));
// → ["pegasus"] // grounded answer, with the edges that proved it.4. Information Graph — your content map
Information Graph. Maps your content ecosystem onto the entity graph and onto real search demand: which URL covers which entity, targets which query, and links where. This is where SEO meets the knowledge layer.
Graph data (text) — 7 entities, 7 relationships
- “Best Road Running Shoes 2026” —mentions→ Nike Pegasus 41
- “Best Road Running Shoes 2026” —mentions→ Hoka Clifton 9
- Pegasus 41 Review —about→ Nike Pegasus 41
- Road Shoes Category —lists→ Hoka Clifton 9
- “Best Road Running Shoes 2026” —internal-link→ Pegasus 41 Review
- “Best Road Running Shoes 2026” —targets→ “best road running shoes”
- Pegasus 41 Review —targets→ “nike pegasus 41 review”
Information Graph
aka Content graph · Asset graphAn information graph maps your content ecosystem — pages, sections, and assets — onto the entities they cover and the search demand they target, plus the links between them.
This is the applied layer SEOs live in. Where a knowledge graph models the world, an information graph models your representation of it: which URL covers which entity, which query each page targets, how pages link to each other, and where the coverage gaps are. It is the bridge between the knowledge layer and your actual site. (The term is an industry/applied one rather than a formal computer-science category — useful precisely because it names the content-to-entity-to-demand mapping that nothing else does.)
- Analogy
- A library's card catalog cross-referenced with what patrons actually ask for. It tells you not just what books exist, but which shelf covers which topic and what readers keep requesting that you don't stock.
- Example
- The page /guides/road-shoes —mentions→ Pegasus 41 and Clifton 9, —targets→ the query “best road running shoes,” and —internal-links→ /reviews/pegasus-41.
- Role for AI agents
- An information graph is how you make every important asset discoverable, crawlable, and mapped to demand. For AEO/GEO it determines whether an agent can find the right page, section, or table to extract and cite when it answers.
5. Context Graph — the decision layer
Context Graph. Layers user, intent, journey stage, location, freshness and trust over the knowledge graph — then scores which answer wins for THIS person at THIS moment. The decision layer for AEO/GEO and agents.
Graph data (text) — 8 entities, 8 relationships
- Nike Pegasus 41 —suitedFor→ Road Running
- Hoka Clifton 9 —suitedFor→ Road Running
- User —in→ Decision stage
- User —at→ Atlanta · in-stock
- User —best-match (0.92)→ Hoka Clifton 9
- Review < 30 days —boosts→ Hoka Clifton 9
- Verified retailer —boosts→ Hoka Clifton 9
- User —match (0.61)→ Nike Pegasus 41
Context Graph
aka Situational graph · Intent graphA context graph layers situation — user, intent, journey stage, location, time, freshness, and trust — over a knowledge graph to decide which answer is right for this person, right now.
A knowledge graph knows that both the Pegasus and the Clifton are road shoes. A context graph decides which one to surface for a beginner marathoner in Atlanta, on mobile, in winter, who wants maximum cushioning and an in-stock option with a fresh review. It re-weights the knowledge graph by situational signals and scores the best match. (Like “information graph,” context graph is an applied/industry framing rather than a formal CS category — useful precisely because it names the situational-relevance layer that nothing else does. Similarity scores below are illustrative.) This is the highest-value and newest layer — the one that powers personalization, AI Overviews, and agentic decisions.
- Analogy
- A great concierge. They know the full menu (the knowledge graph), but their recommendation changes based on who you are, the occasion, the time of day, and what's fresh in the kitchen.
- Example
- User intent=buy, stage=decision, location=Atlanta, season=winter → the context graph scores Hoka Clifton 9 at 0.92 (max cushioning, in-stock locally, review < 30 days) over the Pegasus at 0.61.
- Role for AI agents
- The context graph is the decision layer for AEO, GEO, and agents. It is what lets ChatGPT, Gemini, Perplexity, and AI Mode synthesize the single most relevant answer for a specific task and moment, rather than a generically correct one.
6. Vector / Embedding Index — the similarity space
Vector / Embedding Index. No typed relationships — the dashed lines are similarity scores (cosine distance), not semantic edges. Meaning lives in the positions: things close to the query are retrieved, things far away aren’t. Fast and fuzzy; can’t tell you why two items match or guarantee a fact.
Graph data (text) — 6 entities, 3 relationships
- “cushioned shoes for my first marathon” —0.91→ Hoka Clifton 9
- “cushioned shoes for my first marathon” —0.88→ Nike Pegasus 41
- “cushioned shoes for my first marathon” —0.85→ Hoka Bondi 8
Vector / Embedding Index
aka Vector database · Embedding store · Semantic indexA vector index stores content as numerical embeddings and retrieves by similarity — nearest neighbors in meaning-space — with no schema and no explicit relationships.
A vector index is the counterpart to a graph, not a kind of it. Instead of typed edges, it places everything as points in a high-dimensional space where closeness means semantic similarity. Production systems use approximate nearest-neighbor search (HNSW, IVF) to find the closest content in milliseconds. Most vector databases (Pinecone, Weaviate, Qdrant) also let you attach metadata for filtering — but the geometry itself carries no semantic relationships: it can't tell you why two things relate, can't guarantee a fact, and can't traverse a chain of reasoning. Leading agentic systems are increasingly pairing vector retrieval (recall) with a knowledge graph (precision and grounding) — a pattern Microsoft Research labeled GraphRAG in 2024.
- Analogy
- Standing in a room where similar ideas naturally cluster together. You can grab whatever is nearby, but no one has labeled the connections — you only know things are 'close,' not how they relate.
- Example
- The query “cushioned shoes for my first marathon” lands near the Clifton 9 (0.91) and Pegasus 41 (0.88) and far from a hiking sandal — even though none of those exact words appear in the product names.
- Role for AI agents
- Vector retrieval is how agents find candidate content quickly and tolerate messy, natural-language queries. It is the recall engine of RAG. Pair it with a graph when answers must be exact, explainable, or fact-checked.
// GRAPH: follow typed edges — exact, explainable, but rigid
kg.outNeighbors("pegasus"); // ["nike", "road", ...] — known facts
// VECTOR: rank by cosine similarity — fuzzy, forgiving, but opaque
const q = embed("cushioned shoes for my first marathon");
const hits = index
.map((item) => ({ id: item.id, score: cosine(q, item.vector) }))
.sort((a, b) => b.score - a.score)
.slice(0, 3); // [clifton 0.91, pegasus 0.88, bondi 0.85]
// Note: no edge says WHY they match — only that they're close.Six structures side by side: the comparison table
The whole landscape in one table. Read it as a progression from “files things” to “decides what's relevant” — with the vector index as the parallel, fuzzy alternative to explicit edges.
| Type | What it is | Answers | Structure | Example | Best for | Limitation |
|---|---|---|---|---|---|---|
| Taxonomy | A classification tree | “Where does this belong?” | Single-parent hierarchy; only is-a edges | Footwear → Running → Road → Pegasus 41 | Navigation, browsing, categories | Can't express non-hierarchical relationships |
| Ontology | The schema / blueprint | “What can exist and how do things relate?” | Classes + relationship types + rules (no instances) | Product hasBrand Brand; suitedFor Activity | Shared meaning, interoperability, inference | Holds no data on its own |
| Knowledge Graph | A web of facts | “What is true about these entities?” | Entities + typed, directed edges; resolvable | Pegasus 41 madeBy Nike; sameAs Wikidata | Factual grounding, entity SEO, reasoning | Costly to build & maintain; rots without an ontology |
| Information Graph | Your content ↔ demand map | “What content covers what, for which query?” | Pages/URLs ↔ entities ↔ queries + internal links | /guides/road-shoes targets “best road shoes” | Content strategy, internal linking, AEO coverage | Applied term; only as good as your content audit |
| Context Graph | The relevance / decision layer | “Which answer is right for THIS person now?” | Knowledge graph + user/intent/time/trust weights | Beginner + Atlanta + winter → Clifton 9 (0.92) | Personalization, AEO/GEO, agentic answers | Newest/hardest; needs live signals |
| Vector Index | A similarity space (not a graph) | “What is most similar to this?” | Embeddings; nearest-neighbor by distance; no schema | “marathon cushioning” ≈ Clifton 9 (0.91) | Fuzzy recall, RAG retrieval, search | No facts, no explanations, no traversal |
When to use a knowledge graph vs vector database vs ontology
You need clean navigation and categories
→ Taxonomy — start here; it's the cheapest structure and the foundation for the rest.
You're integrating data across systems or want consistent meaning
→ Ontology — define the shared vocabulary and rules first, even a lightweight one (schema.org).
You want AI engines to understand your entities and cite your facts
→ Knowledge graph — model entities and relationships, and link them out with sameAs.
You're planning content, internal linking, or AEO coverage
→ Information graph — map pages → entities → queries and find the gaps.
You're optimizing for AI Overviews, personalization, or agents
→ Context graph — layer intent, location, freshness, and trust to win the single best answer.
You need fast, fuzzy retrieval over lots of content (RAG)
→ Vector index — for recall; pair it with a knowledge graph for precision and grounding.
The pattern that wins for agents
Modern AI search isn't graph or vectors. The strongest agentic systems use a vector index for recall (find candidate content fast), a knowledge graph for grounding (verify facts and relationships), and a context graph for relevance (pick the right answer for the moment) — all sitting on an ontology that keeps meaning consistent. Build the layers; don't pick one.
Go deeper
Primary sources & further reading
If you want to go deeper than this guide — or verify any claim above — these are the canonical references.
- —Gruber, T. (1993). A Translation Approach to Portable Ontology Specifications — the paper that gave us the now-canonical definition: an ontology is a “formal, explicit specification of a shared conceptualization.”
- —W3C OWL 2 Web Ontology Language — the formal standard for ontologies on the web (T-Box / A-Box, classes, properties, individuals, axioms).
- —W3C RDF 1.1 and SPARQL 1.1 — the standards behind RDF triple stores and the query language used to traverse them.
- —schema.org (2011–present) — the practical, web-scale vocabulary jointly stewarded by Google, Microsoft, Yahoo, and Yandex; the easiest entry point to publishing structured data.
- —Singhal, A. (2012). Introducing the Knowledge Graph: things, not strings (Google blog) — the post that mainstreamed the term “knowledge graph.”
- —Wikidata (2012–present) and Google's Knowledge Graph API — the two reference graphs your entities should resolve to via
sameAs. - —Lewis et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — the original RAG paper.
- —Edge et al. (Microsoft Research, 2024). From Local to Global: A Graph RAG Approach to Query-Focused Summarization — coined the GraphRAG pattern (vectors + KG) referenced above.
- —Malkov & Yashunin (2018). Efficient and robust approximate nearest neighbor search using HNSW graphs — the algorithm behind most modern vector databases.
- —Hogan et al. (2021). Knowledge Graphs — comprehensive academic survey (ACM Computing Surveys), if you want the rigorous version of this guide.
Frequently asked questions
What is a knowledge graph, in plain English?
A knowledge graph is a network of real-world things (entities like Nike, the Pegasus 41, Road Running) connected by labeled, directional relationships (Nike Pegasus 41 → madeBy → Nike). It's how Google's Knowledge Graph, Wikidata, and the answer layer of ChatGPT/Perplexity/Gemini understand that the words you typed refer to specific entities — and what those entities are connected to. Think “Wikipedia for machines, but with the connections made explicit and queryable.”
What is the difference between a knowledge graph and a vector database?
A knowledge graph stores explicit relationships between specific things — Nike Pegasus 41 → madeBy → Nike. You query it by traversing edges. A vector database stores embeddings — long lists of numbers representing each piece of content — and lets you find the most similar content to a query by distance in that number-space (typically using approximate nearest-neighbor search like HNSW). One is precise and explainable; the other is fuzzy and forgiving. They're not competitors — most modern AI systems use vectors for fast recall, then a knowledge graph to verify facts and explain why.
What is GraphRAG and when should I use it?
GraphRAG is the pattern of combining a vector index (for fast, fuzzy retrieval) with a knowledge graph (for grounded facts and explainability). The term was popularized by Microsoft Research's 2024 paper “From Local to Global: A Graph RAG Approach to Query-Focused Summarization.” Use plain (vector-only) RAG when you're answering open-ended questions over messy text and don't need provenance. Use GraphRAG when answers must be exact, auditable, multi-hop (A → B → C), or when you need to reason across an entire corpus rather than just retrieving similar chunks.
When should I use a graph database, an ontology, or a knowledge graph?
A graph database (Neo4j, Memgraph, TigerGraph, ArangoDB) is the storage engine — it holds nodes and edges efficiently. An ontology is the schema you load into (or alongside) it — the rules about what types of things can exist and how they can relate. A knowledge graph is the result: a graph database, structured by an ontology, populated with real instances and ideally linked to external entities via sameAs. You can have a graph DB without an ontology (and watch it rot), or an ontology with no data (a blueprint with no building). A real knowledge graph is all three working together.
Is a knowledge graph the same as an ontology?
No. An ontology is the schema — the classes, allowed relationships, and rules. A knowledge graph is that schema populated with real instances and facts. The ontology is the empty blueprint; the knowledge graph is the populated building. You can have an ontology with no data, but a well-built knowledge graph almost always relies on an ontology to keep its facts consistent as it grows.
Knowledge graph vs vector database — which should I use for RAG?
Usually both. A vector index gives you fast, fuzzy recall: it finds candidate content even when the wording doesn't match. A knowledge graph gives you precision and grounding: exact facts, explainable relationships, and the ability to traverse a chain of reasoning. Most production RAG today is vector-only, but combining vectors (retrieval) with a knowledge graph (verification + structure) — the pattern Microsoft Research dubbed GraphRAG in 2024 — is now standard in agentic systems where answers must be exact, auditable, or fact-checked. Use vectors alone for similarity search; add a graph when grounding matters.
Is semantic search the same as a vector database?
Semantic search is the capability — finding results by meaning rather than exact keywords. A vector database is one common implementation of it: embed every document, embed the query, return nearest neighbors. But semantic search can also be powered by a knowledge graph (using entity matches and typed relationships) or, more powerfully, by both together — vectors for fuzzy recall + a graph for entity grounding. Pure-vector semantic search is fast and forgiving but blind to facts. Add a knowledge graph and you get explainable, fact-checked answers — the AEO/GEO ceiling.
What is a context graph and why does it matter for AEO and GEO?
A context graph adds a situational layer — user, intent, journey stage, location, time, freshness, and source trust — on top of a knowledge graph, then scores which answer is most relevant for a specific person at a specific moment. It matters for Answer Engine Optimization and Generative Engine Optimization because AI systems like ChatGPT, Gemini, Perplexity, and Google's AI Mode don't return ten links — they synthesize one best answer. The context graph is the layer that decides which answer that is.
Do I need an ontology to have a knowledge graph?
Technically no, but practically yes for anything that has to scale or stay correct. Without an ontology you can still link entities, but nothing enforces consistency — the same relationship gets modeled five different ways and the graph rots. A lightweight ontology (even schema.org) gives you a shared vocabulary and rules that keep the graph clean as it grows.
What's the difference between an information graph and a knowledge graph?
A knowledge graph models the world — real entities and the facts that connect them. An information graph models your representation of the world — which of your pages and assets cover which entities, target which queries, and link to each other. The knowledge graph is about truth; the information graph is about coverage. SEO and content strategy live in the information graph; entity understanding lives in the knowledge graph.
Property graph or RDF triple store — does the implementation matter?
Both are valid ways to build a knowledge graph, and the choice is about trade-offs, not correctness. A labeled property graph (Neo4j-style) treats edges as first-class objects with their own properties and is ergonomic for traversal-heavy applications. RDF triple stores use subject–predicate–object triples, follow W3C standards (RDF 1.1, SPARQL 1.1, OWL 2), and excel at linked-data interoperability — sharing facts across organizations and resolving to the public web. Choose property graphs for internal app logic and analytics; choose RDF when web-scale interoperability and standards compliance matter.
Knowledge graph vs relational database — when do I need one over the other?
Use a relational database (Postgres, MySQL) when your data fits clean tables, your queries are well-known up front, and you mostly aggregate within a single domain (orders, users, inventory). Use a knowledge graph when relationships are first-class (you frequently ask who is connected to what, how many hops away?), when entities span domains and need to resolve to the wider web, or when downstream systems — including LLMs — need to reason over the connections, not just join them. Many production stacks use both: the relational DB is the system of record, and a knowledge graph projects the relationships AI systems and answer engines actually consume.
How do I actually build a knowledge graph?
Five steps, in order: (1) define a lightweight ontology — even just schema.org types you'll reuse; (2) extract entities and relationships from your source data (LLMs are very good at this now; tools like LangChain's LLMGraphTransformer or Microsoft's GraphRAG pipeline automate it); (3) resolve entities to canonical IDs (Wikidata QIDs, your internal product IDs) so duplicates collapse; (4) store in a graph database (Neo4j, Memgraph, Neptune) or RDF triple store (Apache Jena, Stardog, GraphDB); (5) publish the high-leverage subset as JSON-LD on your pages so search and answer engines can read it directly. Start small, link out via sameAs, and let the ontology evolve.