METAPHACTORY

Eliminate ambiguity – Create context

With semantic knowledge modeling in metaphactory

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Create a digital twin collaboratively & intuitively

Effortlessly collaborate with key stakeholders and domain experts across the business to build your semantic knowledge model.

Building a digital representation of the physical objects, systems or processes in your organization relies on the collaboration between multiple contributors. Modeling becomes streamlined and simplified with an intuitive interface that all end users can interact with, no matter their area of expertise or level of technical experience. The final model helps to significantly scale and speed up decision-making.

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Build an actionable Enterprise Information Architecture

Ground your strategy, planning and coordination of significant enterprise goals with the knowledge you can derive from an enterprise information architecture based on a semantic model.

With a semantic knowledge model at the heart of your enterprise information architecture, you’ll be able to deliver transparency and context to your information landscape, connect siloed systems, link business needs to IT systems, tackle data integration, governance and discovery challenges, and extract valuable actionable insights to support business processes and decisions.

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Leverage public ontologies & standards

Utilize the numerous public ontologies, vocabularies and standards available for public use to enrich and guide, or act as a starting point for your semantic model.

Reusing pre-existing ontologies and vocabularies, such as Schema.org or standards like ISO and IDMP help define how to create an industry- and enterprise-compliant semantic model, add richness and detail to your model, save time, and ensure interoperability and alignment within your organization or with external partners and with customers.

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Semantic knowledge modeling drives smart business decisions

A semantic knowledge model creates a shared understanding of the meaning of your data and metadata, transforming it into actionable knowledge that can be consumed and understood by all relevant people, processes or tools, thereby democratizing knowledge across the enterprise and making it usable for smarter decision-making. metaphactory enables you to capture explicit, domain-specific knowledge collaboratively with domain experts and stakeholders of any level of technical expertise in one central platform.

Your benefits with metaphactory

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EXPLICIT

Explicitly describe business-relevant concepts and processes, capture semantic relations in your knowledge corpus, and create a shared understanding of knowledge in the organization with metaphactory's knowledge modeling.

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UNIVERSALLY INTERPRETABLE

Through metaphactory’s visual modeling interface, your company’s shared knowledge is organized as a graph, and becomes naturally understandable to humans, while also automatically interpreted by machines.

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COLLABORATIVE

Semantic models are built based on the knowledge of various stakeholders who are experts in their domain. With metaphactory, business users, domain experts / SMEs, ontology engineers and taxonomists can actively collaborate on and contribute to the semantic model.

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VISUAL & STREAMLINED

Create concepts and terms, their synonyms, definitions, attributes and semantic relations in an intuitive, visual user interface, involving stakeholders from different departments and streamlining the process and eliminating media breaks.

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FLEXIBLE & REUSABLE

The use of W3C open standards ensures the flexibility, interoperability and reusability of your semantic models and allows you to avoid vendor lock-in. Follow a top-down or bottom-up approach in building a semantic layer, while being able to iterate often and extend your models as needed to cater to new business needs and use cases across additional functions, domains or organizations.

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ENHANCED WITH AI ASSISTS

Built-in AI assists simplify and enhance the modeling process by providing smart insights, guidance through a conversational AI interface to "talk to your model," and helpful suggestions for mapping concepts to the physical data layer.

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Proven solution

Knowledge democratization with an enterprise knowledge graph at Boehringer Ingelheim

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You need to bring business to [the ontology modeling] activity because IT folks lack the domain expertise, of course, and they cannot create this data model for you. Here we started using metaphactory’s visual modeling interface where users can create ontologies and taxonomies in a collaborative way.

Maksim Kolchin

Knowledge Graph Platform Lead, Boehringer Ingelheim

Explainable and trustworthy recommendation systems at a Swedish furniture retailer

In a presentation at KGC 2023, the customer discusses how they built a semantic knowledge graph with metaphactory, explicitly defining the relations between the products they offer and capturing the contextual meaning behind these relations through a semantic model. This semantic model is shaped by subject matter experts who define the relations, attributes and business-specific terminology of each entity.

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Happy customers

Case studies

CASE STUDY - PHARMA & LIFE SCIENCES
Boehringer Ingelheim uses metaphactory to empower domain experts and deliver a seamless experience over interconnected, use case specific and use case...

BLOG POST
In this blog post, we dive into how knowledge graphs play an important role in IKEA's recommendation systems, based on our experience attending two pr...

BLOG POST
In this guest blog post, Tanuja Gupta, explains how knowledge graphs have helped Scania—a world-leading provider of transport solutions— and TRATON GR...

BLOG POST
In this guest post, Felix Engel and Stefan Schlager explain how metaphactory was used as the development framework for AnthroGraph and how the resulti...

BLOG POST
In this blog post, we talk about how we created a knowledge graph-powered out-of-box metaphacts Resource Hub that integrates with our content manageme...

BLOG POST
This blog post is a recap of a presentation held at the 2023 Knowledge Graph Symposium about BKW Energie's smart meter operations, the data challenges...

CASE STUDY - PHARMA & LIFE SCIENCES
As part of an enterprise-wide initiative to provide democratized, fluid, and connected data that drive business insights and decisions, this American ...

PROJECT INSIGHTS - BUSINESS
metaphactory bridges the gap between business users and technical users and allows this international financial services provider to build harmonized ...

PROJECT INSIGHTS - ENERGY
This German energy company leveraged metaphactory and its visual ontology modeling capabilities to build a domain modeling application for the energy ...

A glance behind the scenes

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Visual semantic modeling

  • Visual semantic modeling interface delivers a user-friendly environment for creating, importing, extending & editing, exploring, visualizating and documenting semantic models, based on an easy to understand, visual language. Technical and non-technical users alike can create or modify classes, relations and attributes in a visual manner
  • The visual language translates to core elements of OWL and SHACL and results in a semantic model based on open modeling and validation language W3C standards
  • Metadata curation and model cataloging for search and full model governance
  • Tight integration between semantic models and vocabularies supports linking classes in the modeling interface to controlled vocabularies

This approach to semantic modeling does not only significantly accelerate the modeling process, but it also allows for the active involvement and participation of multiple user roles, from knowledge graph engineers to domain experts / SMEs, and business users. Additionally, it improves the quality of models and ensures early buy-in from the relevant stakeholders who are later on expected to contribute to or consume the semantic models.

More generally, using semantic modeling, organizations can make implicit information explicit by conveying context and meaning for unambiguous interpretation by both humans and machines. By following standards for interoperability, whatever has been modeled cannot only be interpreted by humans bus also queried, reasoned about, validated, serialized, exchanged and interpreted across a variety of tools and systems. This ultimately provides a foundation for reasoning that AI applications can operate with.

Vocabulary & taxonomy management

  • Intuitive, form-based interface supports domain experts / SMEs and business users in creating and editing SKOS vocabularies to capture business-relevant terms. This helps model domain-specific knowledge in terms that business users understand and can use for analysis and in answering critical business questions.
  • Support for hierarchical lists (hypernyms and hyponyms) and management of multilingual synonyms and symbols
  • Performant tree visualization of and search across term hierarchies
  • Ability to import vocabularies created using external tools and export vocabularies for use outside of metaphactory, simplifying communication between stakeholders
  • Vocabulary cataloging for search, versioning (Git) and metadata curation
  • Tight integration between ontologies and vocabularies supports linking classes in the modeling interface to controlled vocabularies

Data catalog integration

  • Creation, management or import of existing dataset metadata at integration time, making such context metadata an integral part of the connected knowledge graph
  • Support for DCAT and Dublin Core
  • Exposure of dataset metadata in end-user oriented search interfaces, knowledge panels and custom dashboards, delivering vital context information for domain experts / SMEs and allowing them to support day-to-day tasks with traceable insights

Integration with public ontologies & vocabularies

  • Ability to import or provide federated virtual access over multiple data sources
  • Ability to import public ontologies and vocabularies to bootstrap semantic models and vocabularies in metaphactory. Examples of such public ontologies and vocabularies from various verticals include: the HCLSIG/PharmaOntology, the MeSH ontology, the IDMP ontology, the STW thesaurus for economics, Bibframe, schema.org, ISO15926-14, FIBO, and many more
  • Option to use these assets as a basis for an own semantic model, extend them to fit specific needs, or implement them as extensions to own, proprietary knowledge models

Publishing of semantic models

  • Easy and streamlined publishing of semantic models via API and through a Web application, supporting knowledge democratization by ensuring that everyone in the organization can access relevant models
  • SSO integration to ensure compliance with company policies when it comes to user access and management
  • Templating engine allows to optionally refine how information should be presented to different user groups
  • Flexible, open formats ensure that semantic models can be published and shared within the community or for reuse and analysis in pre-competitive research

Collaboration & asset governance

  • Collaborative environment and streamlined modeling experience based on an agile and iterative process, allowing all relevant stakeholders – from knowledge graph engineers and taxonomists to domain experts / SMEs and business users – to equally contribute to the knowledge engineering process and collaborate on defining and continuously improving the semantic model(s), while eliminating obstacles such as external expert tools, media-breaks, or synchronization issues
  • Cataloging, import/export, versioning and metadata management for semantic models, vocabularies and datasets. This improves access to assets across the organization, fosters reuse and helps to build governance processes that scale across individual projects in an organization.
  • Lifecycle and change management for semantic models via a versioning mechanism and an editorial workflow that allows users to explicitly change the status of a model (from 'In development' to 'In review', etc.) lock or unlock a model for review, and communicate with other users about the changes or provide feedback about the current status
  • Git integration for asset versioning, supporting the seamless embedding of knowledge modeling into governance and CI/CD processes
  • Notification functionality allows to send updates to users (e.g. via email) or downstream systems on any actions or status changes related to the workflow or lifecycle of a semantic model or a vocabulary, thus enhancing the transparency and communication around asset management
  • Detailed provenance documentation of an asset’s creation, owner, history and changes, made available in the visual interface, supporting traceability and helping preserve the quality of a semantic model
  • Roles and permissions management enforces security, ensures that only permitted people are able to view, edit or create a semantic model, and enables accountability and traceability

AI-assisted semantic modeling

  • Built-in AI assists to simplify and enhance the modeling process by providing smart insights and helpful suggestions for mapping concepts to the physical data layer
  • Conversational AI interface enabling users to “talk to their model” and lead contextualized conversations with the knowledge graph

Platform built on open standards

metaphactory utilizes the following standards based on RDF, the flexible, open standard for data representation and storage.

OWL

Ontology Language

metaphactory uses the OWL ontology language for the formal definition of your domain model

SHACL

Rules & Constraints

metaphactory uses the SHACL language to define explicit cardinalities & constraints in the model for automated reasoning

SKOS

Vocabularies

metaphactory uses SKOS for categorizing and classifying data in hierarchical vocabulary / taxonomy structures

W3C DCAT

Dataset descriptions

metaphactory uses the DCAT vocabulary for describing datasets & to make the data discoverable, accessible & traceable

Dublin Core

Data cataloging standards

metaphactory uses standardized metadata elements from Dublin Core that offer expanded cataloging information

Try it for yourself!

Semantic knowledge modeling resources