[GTM glossary](/glossary.txt) Ontology Contextual Intelligence & AI # Ontology In short An ontology is a formal description of the types of things in a domain, the properties they have, and the relationships between them. It defines what a "company", "contact", or "industry" is and how they connect. In B2B go-to-market, an ontology gives data and AI a common language. It lets a system know that two job titles mean the same role, or that a subsidiary belongs to a parent. It is more structured than a simple list of tags. ## What an ontology contains * **Entities:** The main object types, such as company, person, product, and event. * **Attributes:** Properties of each entity, such as employee count or location. * **Relationships:** How entities connect, such as works at, owns, or competes with. * **Rules:** Constraints, such as a person has one current employer at a time. --- ## Ontology vs. taxonomy vs. knowledge graph A taxonomy is a hierarchy, like industry and sub-industry. An ontology adds many kinds of relationships and rules on top. A knowledge graph is the actual data stored using that model: the real companies and links between them. --- ## Why it matters for GTM and AI * **Consistent segmentation:** Everyone means the same thing by "mid-market" or "fintech". * **Better matching:** Systems can match records and roll up subsidiaries to parents. * **Grounded AI:** A model that reads a defined structure makes fewer category mistakes than one guessing from raw text. --- ## How to start small ### List core entities Begin with the few objects your team reports on: account, contact, opportunity, signal. ### Agree definitions Write one sentence for each term and each stage. Say "a customer is an account with a paid contract" so sales, marketing, and finance count the same. ### Map relationships Decide how accounts connect to parents, contacts, and products, then enforce it in your CRM fields. --- ## Common mistakes * **Overbuilding:** A huge model nobody maintains is worse than a small one used daily. * **No owner:** Without an owner, definitions drift between teams. * **Mixing levels:** Treating a person, a role, and a title as the same thing. --- ## Ontology FAQs **What is an ontology in data and AI?** An ontology is a structured description of the concepts in a domain, their properties, and how they relate. It gives data systems and AI a shared vocabulary so that terms like company, role, and industry mean the same thing everywhere. **What is the difference between an ontology and a taxonomy?** A taxonomy organizes items into a hierarchy, such as industry and sub-industry. An ontology goes further by defining many types of relationships and rules between concepts, such as which entities connect to which and how. **How is an ontology used in B2B sales and marketing?** It standardizes definitions for accounts, contacts, and segments, helps match records and roll up subsidiaries, and gives AI tools a clear structure to work from. That leads to more consistent reporting and fewer classification errors. **Do you need an ontology to use AI in GTM?** Not to begin, but shared definitions help as use grows. Even a short written list of entities, fields, and meanings improves consistency. Build a small version first and expand only where confusion actually causes problems. Share term --- ## More related phrases * [Context layer](/glossary/context-layer.txt) * [Company embeddings](/glossary/company-embeddings.txt) * [Embeddings](/glossary/embeddings.txt) ## Similar phrases * [Business ontology](/glossary/business-ontology.txt) A business ontology is a structured model of the concepts in a company or market, such as accounts, people, products, and their relationships, so software can interpret data consistently. * [Knowledge graph](/glossary/knowledge-graph.txt) A knowledge graph stores entities such as companies and people as nodes and their relationships as links, giving search and AI a structured, queryable map of facts. * [Context graph](/glossary/context-graph.txt) A context graph links accounts, people, events, and decisions into one connected map, so GTM teams and AI agents can see how each fact relates to the others. * [Semantic search](/glossary/semantic-search.txt) Semantic search finds results based on the meaning and intent of a query rather than exact keyword matches, so related wording and descriptions return relevant results. * [Contextual intelligence](/glossary/contextual-intelligence.txt) Contextual intelligence is the ability to interpret data in light of its surrounding situation, so GTM teams act on what a signal means for a specific account, not just that it exists.