Building Your Knowledge Graph

With your entities identified, the next task is turning that inventory into a knowledge graph you can implement. This lesson explains how to move from the BRIDGE framework’s entity blueprint to a prioritised, well-documented set of entities, types, relationships and attributes. It is the theory behind the Knowledge Graph Foundation workbook, which has its own practical walk-through later in the module.

What this lesson covers

• Reviewing the entity inventory created with the BRIDGE framework
• The information you need for organisations, people, products, services and concepts
• A three-tier prioritisation model: core, authority and extended
• A decision framework for placing entities in the right tier
• Assigning Schema.org types and mapping relationships
• Preparing attributes and gathering data with a template spreadsheet

The lesson starts with what a ready inventory looks like: the organisation’s legal name, founding date, locations and mission; the people who matter, including founders, leadership and key employees; the products and services you offer, with features and pricing; and the concept entities that define your industry, methods and intellectual property. An initial relationship map captures who works where, who founded what, who knows whom and how products relate. A checklist helps you confirm the inventory is ready before you implement anything.

Because not all entities are equal, the lesson groups them into tiers. Core brand entities, such as the main organisation, founders, leadership and primary location, establish the brand and are where you start. Authority entities, such as core products and services, key team members and owned concepts, show depth of expertise. The extended network, including all team members, product features and general industry topics, widens your discovery surface, but only after the core is solid. A simple decision framework uses three questions: is the entity brand-critical, does it have external authority such as a Wikidata or LinkedIn presence, and does it attract high traffic? Data gathering then follows a clear order. You assign each entity a Schema.org type, map relationships such as works for, knows, offers, author and about, and collect the attributes each one needs, from dates, addresses and job titles to image URLs and sameAs candidates such as Wikidata IDs. A downloadable template supports the process.

This lesson is part of the Knowledge Graph Integration and Optimization module and prepares you for the implementation lessons that follow. It is useful for SEOs and marketing teams who want a structured way to plan their brand’s knowledge graph before writing any code.