Knowledge graphs
Definition
A knowledge graph is a structured database that represents entities — people, places, products, concepts — and the relationships between them as an interconnected network of nodes and edges. By encoding facts as connected entity-relationship triples, knowledge graphs power search, recommendation, question answering, and grounded AI understanding.
How it works
A knowledge graph stores information as nodes (entities) and edges (relationships), often expressed as triples of the form subject – predicate – object: for example, "Indexly – is a – company" or "Paris – is capital of – France." Each entity has a stable identity and a set of attributes, and edges describe how entities connect.
Unlike a row-and-column database, a graph makes relationships first-class. This lets systems traverse connections — finding all products by a brand, everyone who worked on a project, or the chain linking two concepts — and answer questions that depend on context and structure rather than a single record.
Large public knowledge graphs power features like search engine entity panels, while organizations build private graphs over their own data. Increasingly, knowledge graphs are paired with language models in graph- based retrieval to provide structured, verifiable grounding.
Why it matters for AI search
Language models are strong at language but can be fuzzy about facts. Knowledge graphs supply precise, structured truth: unambiguous entities, explicit relationships, and traceable facts. When an AI system grounds an answer in a knowledge graph, it can resolve which "Apple" or "Mercury" is meant and follow relationships to assemble a correct, explainable response.
For content owners, this elevates entities and structured data. Clearly identifying the entities your content is about, marking up relationships with schema, and being consistently associated with your topic helps AI systems place you correctly in their understanding of the world — which influences when your brand is recognized, recommended, and cited.
Frequently asked questions
What is a knowledge graph?
A knowledge graph is a structured representation of entities and the relationships between them, stored as a network of nodes and edges. It encodes facts as connected triples, making relationships easy to traverse and reason over.
How are knowledge graphs different from regular databases?
Traditional databases store data in tables and treat relationships as joins. Knowledge graphs make relationships first-class connections between entities, which makes traversing and reasoning over connected facts far more natural.
How do knowledge graphs help AI systems?
They provide structured, verifiable facts and clear entity identities that complement a language model's fuzzy linguistic knowledge. Grounding answers in a graph helps resolve ambiguity and produce explainable, accurate responses.
How can content benefit from knowledge graphs?
Clearly identifying the entities your content covers, using schema markup to express relationships, and being consistently associated with your topic helps AI systems map you correctly, which affects when your brand is recognized and cited.
Schema markup
Schema markup is structured data added to web pages using the schema.org vocabulary that tells search engines and AI systems exactly what the content represents — a product, an article, a recipe, an FAQ, a person. It powers rich results in Google, drives entity understanding in knowledge graphs, and increasingly determines whether content is cited in AI Overviews and LLM-generated answers.
AI grounding
AI grounding is the practice of anchoring an LLM's response in retrieved, citable sources at inference time — instead of letting the model rely solely on its training memory. Grounding is what separates a hallucination-prone chatbot from a search-grade AI assistant like Perplexity, Google AI Overviews, Bing Chat, or retrieval-augmented ChatGPT.
Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is an AI architecture that gives a large language model real-time access to external documents at query time — retrieving relevant passages from a vector database or search index and inserting them into the model's context before it generates a response. RAG is the foundation of modern AI search and the most effective technique for reducing hallucination.
AI brand mentions
AI brand mentions are the instances of your brand name appearing inside responses generated by AI assistants — ChatGPT, Claude, Gemini, Perplexity, Grok, and Google AI Overviews. Unlike traditional brand monitoring across social and press, AI mentions surface inside the answer a buyer is reading, making them a high-leverage demand signal for Generative Engine Optimization (GEO).
Vector search
Vector search is a retrieval method that finds information by comparing numerical meaning representations called embeddings, rather than matching exact keywords. Queries and documents are converted to vectors, and the system returns items whose vectors are closest in space — surfacing semantically relevant results even when the wording differs.
Natural language processing (NLP)
Natural language processing is the AI discipline that enables computers to understand, interpret, and generate human language. It spans tasks such as translation, summarization, sentiment analysis, entity recognition, and question answering. Once driven by hand-built rules and statistical models, NLP is now dominated by large language models built on the transformer architecture.