KMWorld Leader Group for 2025

KNOWLEDGE GRAPHS

By representing data as interconnected concepts, entities, events, and relationships, knowledge graphs provide a comprehensive view of information, enabling organizations to uncover insights, patterns, and trends previously obscured by the noise of large datasets. Businesses use knowledge graphs to link and integrate data (most of which exists in silos) and form multiple interconnections between various data sources, whether it’s complex or simple, structured, or unstructured data.

♦ Winner: Franz

• Enterprise Knowledge
• Empolis Information Management GmbH
• Expert.ai
• Graphwise
• Graphifi
• Lucidworks
• metaphacts
• Neo4j
• Squirro
• Stardog
• TigerGraph
• TopQudrant

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KNOWLEDGE MANAGEMENT

Knowledge management (KM) is the structured process an organization uses to capture, create, organize, share, and utilize its collective knowledge to improve decision making, innovation, and productivity. Effective KM relies on a combination of people, processes, and technology to ensure that relevant information and expertise are readily available to employees when and where they need it, fostering a culture of of continuous learning and better business outcomes.

♦ Winner: Access Innovations

• Atlassian
• Bloomfire
• Domo
• Earley Information Science
• eGain
• Evalueserve
• FireOak Strategies
• Glean Technologies
• HubSpot
• KMS Lighthouse
• MangoApps
• Stravito

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RAG SOLUTIONS

Retrieval-augmented generation (RAG) is the process of optimizing the output of a large language model (LLM), so it references an authoritative knowledgebase outside of its training data sources before generating a response. RAG extends the already powerful capabilities of LLMs to specific domains or an organization’s internal knowledgebase, all without the need to retrain the model. It is an effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.

♦ Winner: Databricks

• Coveo
• Franz
• Glean Technologies
• Graphwise
• LangChain
• Loopio
• NiCE
• Progress
• Shelf.io
• Sinequa
• Vectara
• Vertesia

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SEMANTIC LAYERS

A semantic layer serves as a unified, business-friendly interpretation of complex technical data and disparate knowledge assets, acting as a conceptual bridge between raw data and end users—translating data into meaningful business concepts, relationships, and consistent definitions. The goal of using semantic layers is creating a single source of truth that empowers both human users and AI applications to understand, access, and analyze information without needing deep technical expertise. Key components include knowledge graphs, ontologies, taxonomies, and metadata to organize, connect, and provide business context to all types of organizational knowledge.

♦ Winner: Dremio

• AtScale
• Cube
• dbt Labs
• Denodo
• Enterprise Knowledge
• Graphwise
• HCLSoftware
• Illumex
• Kyvos Insights
• ServiceNow
• ThoughtSpot
• Zetaris