Knowledge Graph
Glean's comprehensive model of all the content, people, and activity within an enterprise connects and contextualizes information to enable AI systems to understand relationships across an organization's digital landscape. The global knowledge graph market is projected to reach $6,938.4 million by 2030 from $1,068.4 million in 2024, representing a compound annual growth rate of 36.6%.
What is a Knowledge Graph?
A knowledge graph maps the relationships between people, content, and activities across your organization. Think of it as a digital blueprint that shows how everything in your company connects — from who worked on which project to which documents relate to specific teams or initiatives. Gartner forecasted that 80% of data and analytics innovations will be made using graph technology by 2025, compared to just 10% in 2021.
Unlike traditional databases that store information in isolation, a knowledge graph understands context. It knows that when someone searches for "benefits," they likely want the official HR policy, not a casual Slack mention. It recognizes that your recent collaborators' documents might be more relevant to your current work than older files from distant teams.
How Knowledge Graphs Work
Knowledge graphs operate by creating connections between three core elements:
PeopleThe graph maps relationships between employees, teams, and organizational structures. It understands reporting hierarchies, project collaborations, and communication patterns to surface the most relevant experts and stakeholders.
Content Documents, presentations, code repositories, and other materials are connected based on topics, projects, and usage patterns. The graph identifies which content is authoritative, recent, or personally relevant to each user.
ActivityUser interactions — what people view, edit, share, and discuss — create behavioral signals that strengthen the graph's understanding of relationships and relevance.
These connections enable AI systems to provide contextually aware responses. When you ask about a project status, the knowledge graph helps surface not just project documents, but also identifies key team members and recent activity around that initiative. For example, LinkedIn reported a 78% accuracy improvement in their customer service AI by incorporating enterprise knowledge graphs with retrieval-augmented generation systems, while simultaneously reducing issue resolution times by 29%.
Knowledge Graphs in Enterprise Search
Traditional enterprise search treats each piece of content as an isolated item. A knowledge graph changes this by understanding the web of relationships that give content meaning and authority within your organization.
For example, when searching for "quarterly planning," a knowledge graph-powered system can:
- Prioritize official planning documents over casual mentions
- Surface content from your team or related departments first
- Identify subject matter experts who can provide additional context
- Connect related materials like budget spreadsheets or team OKRs
Creating an effective knowledge graph requires ingesting data from across your organization's applications — email, documents, chat platforms, project management tools, and more. The challenge lies in understanding permissions, maintaining data freshness, and accurately modeling complex organizational relationships. Notably, enterprise natural language questions over SQL databases achieved only 16.7% accuracy with traditional methods, but accuracy increased to 54.2% when using knowledge graph representations—a 37.5% improvement.
Building Enterprise Knowledge Graphs
Creating an effective knowledge graph requires ingesting data from across your organization's applications — email, documents, chat platforms, project management tools, and more. The challenge lies in understanding permissions, maintaining data freshness, and accurately modeling complex organizational relationships.
Glean builds knowledge graphs by:
- Connecting to 100+ enterprise applications to gather comprehensive data
- Respecting existing permissions so users only see information they're authorized to access
- Continuously learning from user interactions to strengthen relationship understanding
- Modeling nuanced enterprise relationships like team structures, project affiliations, and expertise areas
The result is a living map of your organization's knowledge that grows more accurate and useful over time.
Use Cases
Expert Discovery: Quickly identify who has expertise in specific areas based on their content creation, project involvement, and communication patterns.
Contextual Search: Find information that's not just topically relevant, but contextually appropriate for your role, team, and current projects. A Forrester study on enterprise knowledge graph platforms showed an ROI of 320% and total benefits of over $9.86 million over three years, with organizations completing data analytics applications 2-3 times faster.
Content Authority: Distinguish between official policies and casual discussions, ensuring users find authoritative information when they need it.
Cross-functional Collaboration: Discover related work happening in other departments and identify potential collaboration opportunities.
FAQ
How does a knowledge graph differ from a traditional database?
Traditional databases store information in structured tables with predefined relationships. Knowledge graphs create dynamic connections based on meaning and context, allowing for more flexible and intelligent information retrieval.
What data sources feed into an enterprise knowledge graph?
Enterprise knowledge graphs typically ingest data from email systems, document repositories, chat platforms, project management tools, CRM systems, and other business applications to create a comprehensive view of organizational knowledge.
How does the knowledge graph maintain data privacy?
The knowledge graph respects existing application permissions, ensuring users can only access information they're already authorized to see. Relationships are mapped while maintaining the security boundaries established by your existing systems.
How long does it take for a knowledge graph to become effective?
Knowledge graphs improve continuously as they process more data and user interactions. Most organizations see meaningful improvements in search relevance within the first few months as the system learns organizational patterns and relationships.
