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Knowledge Graphs

Knowledge Graphs in Amplifi provide a structured, semantic representation of information by capturing entities and the relationships between them. They transform scattered facts into a connected network, enabling deeper insights and more intelligent data interaction.

What Are Knowledge Graphs?​

A Knowledge Graph is a network of real-world entities — such as people, organizations, locations, and concepts — and the relationships that link them. Unlike flat keyword-based systems, knowledge graphs preserve the meaning and context of your data, making it more discoverable and useful.

Key Concepts​

  • 🧩 Entities: The core units in a graph, representing distinct people, places, organizations, concepts, etc.
  • 🔗 Relationships: Semantic links between entities that reveal how concepts are connected.
  • 🏛️ Communities: Clusters of closely related entities and relationships that reflect higher-order groupings or themes within the data.

Why Use Knowledge Graphs?​

Knowledge Graphs enhance your ability to understand and navigate complex information by:

  • 🔍 Enabling Relationship-Aware Search: Queries can leverage semantic meaning, not just matching words.
  • 🌐 Connecting Disparate Information: Concepts spread across documents become linked through shared entities and relationships.
  • 🧠 Providing Contextual Understanding: Seeing how a piece of information fits into a larger knowledge structure helps uncover meaning and intent.
  • 📊 Supporting Deeper Analysis: Communities and relationships can be used to identify trends, anomalies, or high-level patterns.

Enhanced Search in Amplifi​

Knowledge Graphs play an active role in improving Amplifi’s search capabilities. When a user query is processed by the search tool, Amplifi doesn’t just perform semantic vector search — it also searches through the entity-relationship network formed by the Knowledge Graph.

The agent combines results from:

  • Vector Search: Retrieves content based on semantic similarity.
  • Graph Search: Identifies relevant entities and their connections from the Knowledge Graph.

By merging these two signals, the agent delivers richer and more accurate responses that account for both conceptual similarity and contextual relationships.

Conceptual Benefits​

Knowledge Graphs serve as the foundation for intelligent systems that go beyond surface-level information. They:

  • Represent meaning, not just data.
  • Foster discovery by exposing hidden connections.
  • Enable machine reasoning, opening up possibilities for more advanced automation and AI-driven insights.

When to Use Knowledge Graphs​

Knowledge Graphs are ideal when:

  • Your data contains named entities and meaningful relationships (e.g., contracts, research papers, business documents).
  • You want to explore connections between ideas or people.
  • You aim to power semantic search, intelligent assistants, or advanced analytics.

By modeling knowledge as a graph, Amplifi helps you move from isolated data points to a rich, connected understanding of your information — unlocking smarter, faster, and more relevant insights.