Introduction

When we introduced the Knowledge Graph Engine in SAP HANA Cloud, customers appreciated the ability to manage RDF data alongside relational, spatial, and vector data, all within a single, unified platform.
This multi-model capability is powerful because it allows organizations to:

Bring together structured and unstructured data in one placeConnect data across systems and domainsEnable richer semantic queries and AI-driven use cases

However, one consistent piece of feedback we heard from customers was:
“We can store and query knowledge graphs, but how do we model them easily and explore them visually?”
And that’s exactly what we set out to solve. Before getting into the details, let’s recap on what knowledge graphs are and their use cases.

Knowledge Graphs & their Impact

If you’re new to knowledge graphs, a good starting point is this blog: Connecting the Facts: SAP HANA Cloud’s Knowledge Graph Engine for Business Context

In simple terms, a knowledge graph connects data through relationships. Instead of storing information in isolated tables, it captures how entities such as people, products, locations, and events are related to each other. This becomes especially powerful in today’s AI-driven world, where understanding context and relationships is just as important as accessing raw data.

Business scenarios powered by knowledge graphs

Connecting siloed enterprise data: Bring together data from ERP, CRM, and other systems into a unified, connected view. Instead of querying systems separately, users can explore relationships across the business.Enabling smarter AI and GenAI: Provide the context and relationships that AI models rely on. This improves accuracy, grounding, and explainability of AI-driven outcomes.

Discovering hidden insights: Go beyond “what happened” to understand “how things are connected” — and what that means for your business. When the relationships between your data are visible, you can identify the root cause of a supply chain disruption, understand why a customer segment is underperforming, or spot an 

emerging risk before it becomes a problem. This turns knowledge graphs from a data management tool into a driver of better, faster decisions — with context and evidence to back them up.    Improving search and discovery: Make search more intuitive and context aware. Users can navigate data through relationships, leading to more relevant and meaningful results.Building a semantic foundation: Add meaning and structure to enterprise data. This creates a shared understanding across teams and enables more consistent analytics and applications.

Integration of SAP HANA Cloud and metaphactory for a seamless Knowledge Graph experience

We are excited to share that SAP HANA Cloud Knowledge Graph Engine is now available with integration support on the metaphactory platform.

This brings together the best of both worlds:

A high-performant, enterprise-grade multi-model databaseA powerful, user-friendly knowledge graph modeling and visualization layer

Together, they enable a more complete, end-to-end knowledge graph experience – from managing and connecting data to modeling, exploring, and deriving insights. metaphactory brings a rich set of capabilities that complement HANA Cloud’s storage and query power:

Visual semantic modeling: A user-friendly interface for creating, extending, and documenting ontologies based on open W3C standards (OWL, SHACL), accessible to both technical and non-technical users.Knowledge discovery and exploration: Rich interactive visualizations, graph exploration and semantic search that let users navigate and make sense of complex relationships without writing queries.     Governance and lifecycle management: Versioning, editorial workflows, and collaboration tools that make knowledge graphs production-ready and enterprise-grade.     Open standards foundation: Built on RDF, SPARQL, SKOS, OWL and SHACL, ensuring interoperability, reusability, and flexibility across industries and use cases

Schema data from HANA’s Supplier Relationship Management system loaded into metaphactory

Where Customers Will See the Most Value with the Integration

1. From enterprise data to knowledge graph – faster time to value

Customers already have valuable data in HANA Cloud, across tables, views, and other models.

With the integration on metaphactory, they can now:

Build ontologies without starting from scratchMap relational structures from HANA Cloud into RDFImport RDF datasets or use existing enterprise data

This significantly reduces the effort required to get to a usable knowledge graph, without duplicating data or rebuilding pipelines.

Mappings between the Supplier Relationship Management schema and the Supplier ontology defined in metaphactory

2. AI-assisted & Visual Ontology Modeling – enabling more users, not just experts

Traditionally, building knowledge graphs required deep expertise in RDF and OWL modeling.
On metaphactory, you can:

Define classes, relationships, and properties visually or using an AI assistantIterate on ontology design interactivelyEnable functional users to participate in modeling

With metaphactory’s AI-powered semantic modeling, users don’t need to know ontology languages upfront. Instead, they can describe their domain in plain language through a conversational interface — and the AI assistant proposes classes, relationships, and properties in response. It can surface relevant concepts from uploaded documents, suggest reuse of established public ontologies, and perform quality checks based on proven modeling guidelines (including OWL and SHACL validation) as the model evolves. The result is a modeling process that moves at the speed of thought, while still producing robust, standards-compliant outputs.

This means knowledge graph development is no longer limited to a small group of experts. Functional users and domain experts can actively shape the knowledge graph — describing what they know, reviewing AI suggestions, and refining the model collaboratively. This accelerates adoption, improves model quality through broader stakeholder input, and reduces dependency on specialized skills.

The Supplier ontology visualized in metaphactory’s visual ontology editor

Extending the Supplier ontology using metaphactory’s AI modeling agent

3. Turn complex relationships into intuitive insights

Knowledge graphs are powerful because of the relationships they capture — but those relationships need to be understood and acted upon by the people who matter most: business users, domain experts, and decision makers.

With metaphactory’s visualization and application-building capabilities:

Users can explore connections between entities visually and navigate relationships dynamicallyFilter and drill into relevant data without writing complex queriesVisualize data across multiple dimensions — including interactive graphs, timelines, maps, and chartsConfigure search and exploration interfaces, driven directly by the ontology defined in metaphactory

Customers can move from writing complex queries to visually understanding their data, making insights more accessible across teams.

4. Enterprise-ready lifecycle and governance

Building a knowledge graph is not a one-time activity — it evolves over time.

This integration supports:

Versioning of ontologies and datasetsChange tracking and auditabilityStructured lifecycle management across environments

This makes knowledge graphs production-ready and enterprise-friendly. Organizations can confidently manage knowledge graphs in production environments, ensuring control, governance, and compliance.

5. Extend and enrich with external knowledge

Real-world use cases often require combining internal enterprise data with external knowledge.
With this setup, customers can explore:

Integrating external/public RDF datasetsEnriching internal data with external contextBuilding cross-domain knowledge graphs

This accelerates the development of custom applications by leveraging the SAP HANA Cloud Knowledge Graph Engine, enabling richer insights, and improved contextual understanding, especially for AI and knowledge-driven scenarios.

6. A trusted foundation for AI and GenAI applications

As organizations invest in AI and GenAI, one of the biggest challenges is ensuring outputs are accurate, contextual, and explainable. A knowledge graph stored in SAP HANA Cloud and made accessible through metaphactory provides exactly this — a structured, semantically rich foundation that AI models can reason over with confidence.

With this integration, customers can:

Ground GenAI responses in verified enterprise knowledge, reducing hallucinations and improving accuracyProvide AI agents with the contextual relationships they need to answer complex, multi-hop questions across business domainsDeliver explainable AI outputs — where the reasoning behind an answer can be traced back to real data and relationships in the graphBuild RAG (Retrieval Augmented Generation) pipelines that combine the precision of structured knowledge with the fluency of large language models

For SAP customers already exploring AI-driven use cases, this integration provides the trusted knowledge layer that makes those initiatives enterprise-ready.

Get Started

To get started with SAP HANA Cloud Knowledge Graph Engine and metaphactory, there are a couple of prerequisites and setup options to be aware of.

Prerequisites

An active SAP HANA Cloud instanceKnowledge Graph Engine (Triple Store) enabled on your HANA Cloud instanceSubscription to metaphactory – see options below      

Getting access to metaphactory

There are two ways to get started with metaphactory, depending on your preference:

Option 1 — Hosted Trial (recommended if you’re new to metaphactory)

The easiest way to get started is through a free 4-week hosted trial — no setup or infrastructure costs required. A self-guided tutorial walks you through the key capabilities in under 3 hours, using either your own data or a sample dataset provided. Start your hosted trial here

Option 2 — Self-managed deployment (Docker or AWS Marketplace)

If you prefer to deploy metaphactory in your own environment, two options are available:

Docker: Deploy metaphactory in your own infrastructure and connect it directly to your SAP HANA Cloud instance. This is the most flexible option for teams that want full control over their setup.AWS Marketplace: Run metaphactory in the cloud on AWS and connect it to your SAP HANA Cloud instance. A 2-week trial is available, though AWS infrastructure charges apply.

For both options, use the sap-hana-connector-app to connect your metaphactory instance to your SAP HANA Cloud instance.

Demo

The HANA Cloud relational database used in this demo contains the Supplier Relationship Management (SRM) system, which holds information for suppliers along with their global market regions, departmental contact networks, and critical risk assessment factors. This schema is loaded from HANA Cloud, and explored visually in metaphactory.

As an example, the video walks through the business_partners table and its columns — all rendered and navigated inside metaphactory’s visual interface. The mappings between the SRM schema and an ontology are then defined within metaphactory, showing how the business_partners table is mapped to the Supplier class.

From there, we move into metaphactory’s built-in ontology editor, where the Supplier ontology is presented. The editor allows users to define new classes and establish relationships between them — all without leaving the platform. For teams that prefer a more guided experience, metaphactory’s AI assistant can handle ontology modeling conversationally, streamlining the process further.

Importantly, all changes made in metaphactory are reflected in real time on the underlying data in SAP HANA Cloud Knowledge Graph Engine, while the data itself remains securely in place — eliminating the need for data movement, duplication, or additional ETL processes.

Finally, once the ontology and mappings are in place, metaphactory enables rich visual exploration of the connected data — giving users an intuitive way to navigate and make sense of complex supplier relationships directly within the platform.

The path forward

By combining the strengths of SAP HANA Cloud and metaphactory, we are enabling customers to move beyond just storing knowledge graphs. They can now:

Model them more easilyExplore them more intuitivelyManage them more effectivelyAnd ultimately, derive more value from their data

This is a key step toward making knowledge graphs more accessible, scalable, and impactful across the enterprise. This announcement is just the beginning. We plan to continue this as a blog series, where we will dive deeper into technical capabilities, integration details, and real business use cases enabled by SAP HANA Cloud Knowledge Graph Engine and metaphactory.

Stay tuned and follow along for more detailed insights and hands-on explorations.

Quick Links:

metaphacts & SAP HANA Cloud integration  

​ IntroductionWhen we introduced the Knowledge Graph Engine in SAP HANA Cloud, customers appreciated the ability to manage RDF data alongside relational, spatial, and vector data, all within a single, unified platform.This multi-model capability is powerful because it allows organizations to:Bring together structured and unstructured data in one placeConnect data across systems and domainsEnable richer semantic queries and AI-driven use casesHowever, one consistent piece of feedback we heard from customers was:“We can store and query knowledge graphs, but how do we model them easily and explore them visually?”And that’s exactly what we set out to solve. Before getting into the details, let’s recap on what knowledge graphs are and their use cases. Knowledge Graphs & their ImpactIf you’re new to knowledge graphs, a good starting point is this blog: Connecting the Facts: SAP HANA Cloud’s Knowledge Graph Engine for Business ContextIn simple terms, a knowledge graph connects data through relationships. Instead of storing information in isolated tables, it captures how entities such as people, products, locations, and events are related to each other. This becomes especially powerful in today’s AI-driven world, where understanding context and relationships is just as important as accessing raw data.Business scenarios powered by knowledge graphsConnecting siloed enterprise data: Bring together data from ERP, CRM, and other systems into a unified, connected view. Instead of querying systems separately, users can explore relationships across the business.Enabling smarter AI and GenAI: Provide the context and relationships that AI models rely on. This improves accuracy, grounding, and explainability of AI-driven outcomes.Discovering hidden insights: Go beyond “what happened” to understand “how things are connected” — and what that means for your business. When the relationships between your data are visible, you can identify the root cause of a supply chain disruption, understand why a customer segment is underperforming, or spot an emerging risk before it becomes a problem. This turns knowledge graphs from a data management tool into a driver of better, faster decisions — with context and evidence to back them up.    Improving search and discovery: Make search more intuitive and context aware. Users can navigate data through relationships, leading to more relevant and meaningful results.Building a semantic foundation: Add meaning and structure to enterprise data. This creates a shared understanding across teams and enables more consistent analytics and applications. Integration of SAP HANA Cloud and metaphactory for a seamless Knowledge Graph experienceWe are excited to share that SAP HANA Cloud Knowledge Graph Engine is now available with integration support on the metaphactory platform.This brings together the best of both worlds:A high-performant, enterprise-grade multi-model databaseA powerful, user-friendly knowledge graph modeling and visualization layerTogether, they enable a more complete, end-to-end knowledge graph experience – from managing and connecting data to modeling, exploring, and deriving insights. metaphactory brings a rich set of capabilities that complement HANA Cloud’s storage and query power:Visual semantic modeling: A user-friendly interface for creating, extending, and documenting ontologies based on open W3C standards (OWL, SHACL), accessible to both technical and non-technical users.Knowledge discovery and exploration: Rich interactive visualizations, graph exploration and semantic search that let users navigate and make sense of complex relationships without writing queries.     Governance and lifecycle management: Versioning, editorial workflows, and collaboration tools that make knowledge graphs production-ready and enterprise-grade.     Open standards foundation: Built on RDF, SPARQL, SKOS, OWL and SHACL, ensuring interoperability, reusability, and flexibility across industries and use casesSchema data from HANA’s Supplier Relationship Management system loaded into metaphactory Where Customers Will See the Most Value with the Integration1. From enterprise data to knowledge graph – faster time to valueCustomers already have valuable data in HANA Cloud, across tables, views, and other models.With the integration on metaphactory, they can now:Build ontologies without starting from scratchMap relational structures from HANA Cloud into RDFImport RDF datasets or use existing enterprise dataThis significantly reduces the effort required to get to a usable knowledge graph, without duplicating data or rebuilding pipelines.Mappings between the Supplier Relationship Management schema and the Supplier ontology defined in metaphactory 2. AI-assisted & Visual Ontology Modeling – enabling more users, not just expertsTraditionally, building knowledge graphs required deep expertise in RDF and OWL modeling.On metaphactory, you can:Define classes, relationships, and properties visually or using an AI assistantIterate on ontology design interactivelyEnable functional users to participate in modelingWith metaphactory’s AI-powered semantic modeling, users don’t need to know ontology languages upfront. Instead, they can describe their domain in plain language through a conversational interface — and the AI assistant proposes classes, relationships, and properties in response. It can surface relevant concepts from uploaded documents, suggest reuse of established public ontologies, and perform quality checks based on proven modeling guidelines (including OWL and SHACL validation) as the model evolves. The result is a modeling process that moves at the speed of thought, while still producing robust, standards-compliant outputs.This means knowledge graph development is no longer limited to a small group of experts. Functional users and domain experts can actively shape the knowledge graph — describing what they know, reviewing AI suggestions, and refining the model collaboratively. This accelerates adoption, improves model quality through broader stakeholder input, and reduces dependency on specialized skills.The Supplier ontology visualized in metaphactory’s visual ontology editor Extending the Supplier ontology using metaphactory’s AI modeling agent 3. Turn complex relationships into intuitive insightsKnowledge graphs are powerful because of the relationships they capture — but those relationships need to be understood and acted upon by the people who matter most: business users, domain experts, and decision makers.With metaphactory’s visualization and application-building capabilities:Users can explore connections between entities visually and navigate relationships dynamicallyFilter and drill into relevant data without writing complex queriesVisualize data across multiple dimensions — including interactive graphs, timelines, maps, and chartsConfigure search and exploration interfaces, driven directly by the ontology defined in metaphactoryCustomers can move from writing complex queries to visually understanding their data, making insights more accessible across teams.4. Enterprise-ready lifecycle and governanceBuilding a knowledge graph is not a one-time activity — it evolves over time.This integration supports:Versioning of ontologies and datasetsChange tracking and auditabilityStructured lifecycle management across environmentsThis makes knowledge graphs production-ready and enterprise-friendly. Organizations can confidently manage knowledge graphs in production environments, ensuring control, governance, and compliance.5. Extend and enrich with external knowledgeReal-world use cases often require combining internal enterprise data with external knowledge.With this setup, customers can explore:Integrating external/public RDF datasetsEnriching internal data with external contextBuilding cross-domain knowledge graphsThis accelerates the development of custom applications by leveraging the SAP HANA Cloud Knowledge Graph Engine, enabling richer insights, and improved contextual understanding, especially for AI and knowledge-driven scenarios.6. A trusted foundation for AI and GenAI applicationsAs organizations invest in AI and GenAI, one of the biggest challenges is ensuring outputs are accurate, contextual, and explainable. A knowledge graph stored in SAP HANA Cloud and made accessible through metaphactory provides exactly this — a structured, semantically rich foundation that AI models can reason over with confidence.With this integration, customers can:Ground GenAI responses in verified enterprise knowledge, reducing hallucinations and improving accuracyProvide AI agents with the contextual relationships they need to answer complex, multi-hop questions across business domainsDeliver explainable AI outputs — where the reasoning behind an answer can be traced back to real data and relationships in the graphBuild RAG (Retrieval Augmented Generation) pipelines that combine the precision of structured knowledge with the fluency of large language modelsFor SAP customers already exploring AI-driven use cases, this integration provides the trusted knowledge layer that makes those initiatives enterprise-ready. Get StartedTo get started with SAP HANA Cloud Knowledge Graph Engine and metaphactory, there are a couple of prerequisites and setup options to be aware of.PrerequisitesAn active SAP HANA Cloud instanceKnowledge Graph Engine (Triple Store) enabled on your HANA Cloud instanceSubscription to metaphactory – see options below      Getting access to metaphactoryThere are two ways to get started with metaphactory, depending on your preference:Option 1 — Hosted Trial (recommended if you’re new to metaphactory)The easiest way to get started is through a free 4-week hosted trial — no setup or infrastructure costs required. A self-guided tutorial walks you through the key capabilities in under 3 hours, using either your own data or a sample dataset provided. Start your hosted trial hereOption 2 — Self-managed deployment (Docker or AWS Marketplace)If you prefer to deploy metaphactory in your own environment, two options are available:Docker: Deploy metaphactory in your own infrastructure and connect it directly to your SAP HANA Cloud instance. This is the most flexible option for teams that want full control over their setup.AWS Marketplace: Run metaphactory in the cloud on AWS and connect it to your SAP HANA Cloud instance. A 2-week trial is available, though AWS infrastructure charges apply.For both options, use the sap-hana-connector-app to connect your metaphactory instance to your SAP HANA Cloud instance. DemoThe HANA Cloud relational database used in this demo contains the Supplier Relationship Management (SRM) system, which holds information for suppliers along with their global market regions, departmental contact networks, and critical risk assessment factors. This schema is loaded from HANA Cloud, and explored visually in metaphactory.As an example, the video walks through the business_partners table and its columns — all rendered and navigated inside metaphactory’s visual interface. The mappings between the SRM schema and an ontology are then defined within metaphactory, showing how the business_partners table is mapped to the Supplier class.From there, we move into metaphactory’s built-in ontology editor, where the Supplier ontology is presented. The editor allows users to define new classes and establish relationships between them — all without leaving the platform. For teams that prefer a more guided experience, metaphactory’s AI assistant can handle ontology modeling conversationally, streamlining the process further.Importantly, all changes made in metaphactory are reflected in real time on the underlying data in SAP HANA Cloud Knowledge Graph Engine, while the data itself remains securely in place — eliminating the need for data movement, duplication, or additional ETL processes.Finally, once the ontology and mappings are in place, metaphactory enables rich visual exploration of the connected data — giving users an intuitive way to navigate and make sense of complex supplier relationships directly within the platform.  The path forwardBy combining the strengths of SAP HANA Cloud and metaphactory, we are enabling customers to move beyond just storing knowledge graphs. They can now:Model them more easilyExplore them more intuitivelyManage them more effectivelyAnd ultimately, derive more value from their dataThis is a key step toward making knowledge graphs more accessible, scalable, and impactful across the enterprise. This announcement is just the beginning. We plan to continue this as a blog series, where we will dive deeper into technical capabilities, integration details, and real business use cases enabled by SAP HANA Cloud Knowledge Graph Engine and metaphactory.Stay tuned and follow along for more detailed insights and hands-on explorations.Quick Links:metaphacts & SAP HANA Cloud integration    Read More Technology Blog Posts by SAP articles 

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