METAPHACTORY

Semantic knowledge modeling

Maximum flexibility, agility & speed in defining, extending & publishing domain-specific semantic knowledge models

Do you often struggle because valuable expert knowledge that is critical to smart, sustainable business decisions is hidden in your domain experts' minds?

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Many customers indeed report that if such expert knowledge is represented at all, it is usually burried in proprietary software applications or in long, unstructured documents & PDF files.

Your benefits with metaphactory

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EXPLICIT

Knowledge modeling with metaphactory enables you to explicitly describe domain-specific products, processes, and other company knowledge, capture semantic relations in your knowledge corpus, and create a shared understanding of knowledge in the organization.

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UNIVERSALLY INTERPRETABLE

Through metaphactory’s visual modeling interface, your company’s shared knowledge is organized as a graph, becomes naturally understandable to humans, and can be 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

metaphactory comes with an intuitive and user-friendly modeling interface that supports the creation of concepts and terms, their synonyms, definitions, attributes, and semantic relations in a visual manner. This empowers all relevant stakeholders in the organization.

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FLEXIBLE & REUSABLE

The use of W3C open standards ensures the flexibility, interoperability, and reusability of your semantic model(s) and allows you to avoid vendor lock-in. metaphactory allows you to start small, iterate often, and extend your model as needed, for example, to support the integration of new products, processes, or knowledge and to cater to new business needs and use cases across additional functions, domains, or organizations.

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STREAMLINED

Because it supports the creation, management, governance, versioning, and publishing of knowledge models through one centralized Web-based portal, metaphactory transforms modeling into a streamlined, end-to-end process without media breaks.

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

Happy customers

A glance behind the scenes

Visual ontology modeling

 

metaphactory's visual ontology editor delivers a user-friendly environment for ontology creation, importing, extension & editing, exploration, visualization, and documentation, based on a visual language. Users can create or modify classes, relations, and attributes in a visual manner, and link classes to controlled vocabularies available in the system.

 

The visual language translates to core elements of OWL and SHACL and results in an ontology based on open modeling and validation language W3C standards.

 

This approach to ontology modeling does not only significantly accelerate the modeling process, but it also allows for the active involvement and participation of multiple user roles, from ontology 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 lateron expected to contribute to or consume the semantic models.

 

Vocabulary & taxonomy management

 

metaphactory delivers an intuitive vocabulary management interface that supports domain experts / SMEs and business users in creating and editing SKOS vocabularies and taxonomies 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.

 

Controlled vocabularies created directly in metaphactory or maintained via external tools can be linked to specific classes in the ontology through metaphactory’s ontology editor. This simplifies the communication between stakeholders.

 

Data catalog integration

 

With its data cataloging capabilities, metaphactory makes dataset metadata itself an integral part of the connected graph. Based on open standards (e.g., DCAT, Dublin Core), these capabilities allow ontology engineers and taxonomists to easily create, manage, or import existing dataset metadata and enrich other assets with this additional metadata layer. This adds explicit dataset context information to ontologies and vocabularies.

 

Exposed in the publishing interface, the dataset context information becomes immediately accessible to end users, domain experts / SMEs. It delivers additional and vital context information for end-user tasks and helps drive traceable and trustful business decisions.

 

Integration with public ontologies & vocabularies

 

metaphactory supports import of or federated virtual access over multiple data sources and also allows for the integration of public ontologies and vocabularies. Examples of such public ontologies and vocabularies from various verticals include: the HCLSIG/PharmaOntology, the MeSH ontology, the STW thesaurus for economics, CIDOC-CRM, Bibframe, schema.org, ISO15926-14, FIBO, and many more.

 

These assets can be used as a basis for your own semantic model, can be extended to fit your specific needs, or can be implemented as extensions to your own, proprietary knowledge model.

 

Publishing of semantic models

 

Knowledge democratization requires that everyone in the organization has access to relevant semantic models and vocabularies. metaphactory does not only support the creation and management of such knowledge models, but it additionally allows for easy and streamlined publishing via API and through a web application. This web application becomes a central publishing interface and resource for end users across the entire organization. SSO integration guarantees compliance with company policies when it comes to user access and management, while metaphactory’s templating engine enables you to define which information you want to publish to which user groups.

 

Another important aspect here is publishing and sharing within the community or for reuse and analysis in pre-competitive research. Since the knowledge model is built using flexible, open formats, it becomes human- and machine-understandable, and can be made available to partners or the general public for reuse.

 

Collaboration, versioning & metadata curation

 

metaphactory transforms knowledge modeling into a streamlined, end-to-end experience based on an agile and iterative process. It allows all relevant stakeholders - from ontology engineers and taxonomists to domain experts / SMEs and business users - to equally contribute to the ontology 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.

 

Additionally, metaphactory delivers enhanced support for managing these assets, such as cataloging, versioning, and metadata editing capabilities. The ontology, vocabulary, and dataset catalogs feature capabilities to import, create, manage metadata for, and search and explore. They improve access to assets across the organization, foster reuse, and help to build governance processes that scale across individual projects in an organization.

 

Finally, all assets and changes to these assets can be versioned through Git integration. This enables the seamless embedding of knowledge modeling into governance and CI/CD processes.

 

Enrichment using ML & other data-driven AI

 

Through integration with data-driven algorithms, metaphactory supports the enrichment of explicit knowledge mapped to your semantic model with knowledge learned or derived via machine learning, reasoning, or other techniques. By enriching your knowledge model with information on decision models, metaphactory enables decision makers to move from repetitive daily decisions to an automated decision intelligence solution - a solution that not only provides deep, contextual analytics but also decision support and, ultimately, continuous intelligence and decision automation.

 

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

Use cases

Modeling of company and domain knowledge

» Capture everything from key organizational goals to business processes
» Interlink this information with business operations data, skills and competencies, partner and supplier ecosystem information, or any other domain relevant within the enterprise where relevant knowledge needs to be explicitly modeled

Results: Improve data literacy across the organization and leverage critical domain knowledge for business decisions

Utilization & extension of public ontologies & vocabularies

» Utilize public knowledge, from simple term lists to complete domain models
» Allow for reuse of this knowledge while extending with own terms and definitions & expanding the models into other domains

Results: Extract valuable knowledge from public data sources, foster reuse of internal knowledge, and achieve enterprise-wide & cross-organizational alignment on the concepts and terms, and enable pre-competitive data sharing

Modeling compliant with ISO-IEC standards

» Modeling compliant with ISO-IEC standards
» Follow ISO/IEC standards like ISO 15926, AAS, OPC-UA, and others
» Benefits from a layered approach for standard-compliant & sharable information down to proprietary knowledge defined in domain specific extensions

Results: Ensure interoperability, accessibility, findability, shareability, and reusability of semantic models and deliver one central inventory / digital twin for reuse throughout the whole lifecycle of an asset and for sharing with (sub-)contractors, partners, OEMs, or suppliers

Case studies

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