Assigning types to an entity is an important step to help classify an entity within the knowledge graph. When you assign types, take these things into consideration:

1. Identify the main types to help classify that entity clearly

2. Assign as many types as you’d like, but try and only use types that are relevant to the entity

3. When creating new types, keep them broad enough that other entities can use that type for their own classification

4. If you're using an existing type with a predefined schema, fill in as many property values inherited from that schema

5. If you're creating a new type, add properties that enrich the schema to support others who may use that type

Consistent naming conventions are key to creating a clear, organized knowledge graph. Standardized names for entities, relations, and properties make the knowledge graph easier to understand and navigate.

1. Choose a standard format for naming entities, properties, relations etc. For example, use singular names like ‘Person’ instead of ‘People’ for types, or ‘Role’ instead of ‘Works as a’ for a property name regarding their job title

When you create data in Geo it’s you’re publishing it to a larger knowledge graph of data that’s interconnected.

2. Follow a consistent case style when naming

A knowledge graph is a way of organizing information that connects different pieces of data, like people, places, or things, based on their relationships. Imagine a big network where each node is something like a person or place, and each edge between nodes shows how they’re related, like ‘works with’ or ‘located in’. Creating, editing and reviewing data for a knowledge graph requires careful planning to ensure clarity, consistency, and meaningful connections.

3. Try not to use overly specific names that can’t be used elsewhere unless necessary

This is crucial for building a meaningful and navigable knowledge graph. Well-designed relationships clarify how entities connect, making the graph more intuitive to explore. Clear, versatile relationships also reduce redundancy and enhance data accuracy, making querying for data faster and easier. Consider these best practices when defining relationships:

1. Create clear, meaningful relationships that connect entities. For example this could look like; ‘Person’ works at the ‘Company’ as a ‘Role’.

2. Avoid overly specific or redundant relationships; instead, use versatile ones that can be applied in multiple contexts.

Try and avoid duplicating data wherever possible; if you need to create a relation to an entity that already exists, reference to that existing entity rather than creating a new version of it. This keeps the knowledge graph company and interconnected. You can even merge multiple instances of the same entity together to create one formalized entity.

Editors are essential to maintaining the quality of knowledge graph data. As an editor of a public space, you’ll review edits proposed by others to uphold data accuracy and clarity through the processing of voting governance.

1. Carefully review proposals to understand the changes being made

2. Assess if the proposed edits offer a clearer or more accurate representation than the original data

3. Verify any new claims or sources for accuracy

4. Reject any proposals that may negatively impact the quality or integrity of the knowledge graph