Visual Ontology Modeling for Domain Experts and Business Users with metaphactory

Visual Ontology Modeling for Domain Experts and Business Users with metaphactory
Presentation at the OntoCommons Workshop on Ontology Engineering Tools @ Fri Mar 19, 2021

1. Visual Ontology Modelling in metaphactory
Peter Haase
OntoCommonsWorkshop on Tools for Ontology Engineering
19.3.2021

2. metaphactsat a Glance
§metaphactsGmbH
§Founded in 2014
§Headquartered in Walldorf, Germany
§International team across multiple
locations
§Independent software vendor
§Privately-held, owner-managed company
§metaphactory –Knowledge Graph
Platform
COMPANY FACTS
Drive digital transformation by unlocking
the value of your data assets with
knowledge graphs.
MISSION

3. •Knowledge Graph Modelling
•Visual ontology modelling
•Domain expert targeted KG management
•End-user OrientedInteraction
•Out-of-the-box knowledge graph exploration
•Customization through apps
•Knowledge GraphApplicationBuilding
•Low-code platform with declarative templates
•Data-and model-driven user experience and user interface design
The metaphacts Approach -The Knowledge Graph is at the Core

4. Knowledge Graph Asset Management
KNOWLEDGE GRAPH ASSETS
Ontologies
Vocabularies
Datasets
Mappings
Rules

5. Building the Knowledge Graph
Ontology
Engineer
Domain
Expert
Data
Steward
All stakeholders are
empowered to actively
participate in the modeling
process
Visual Ontology Modeling
Agile processes for
ontology design,
implementation and
documentation
Example Ontology
from the Life Sciences
Domain

6. •Right expressivity
•Class hierarchy, relations, attributes, constraints (domain, range, cardinalities)
•Determined by typical needs in conceptual modelling and data integration
üLanguage for data stewards and subject matter experts
•Language based on standards
•OWL as established ontology language, but lack of constraints
•Increasing relevance of SHACL as modeling language, support for SHACL by major database vendors
üBest of OWL + SHACL
•Visual notation
•Best practices and principles from conceptual modelling and ontology visualization
•Clear correspondence of visual notation with syntax and semantics of the ontology language
üIntegratedontology design, implementation, and documentation
Our Motivation for a Visual Ontology Modelling Language

7. Visual Modelling Notation
1)Classes
2)Relations
3)Attributes
4)Additionalmetadata
Visual Ontology Exploration, Modelling and Documentation

8. •A visual, conceptual language which translates internally to OWL + SHACL
Visual Ontology Exploration, Modelling and Documentation
Full language reference & translation https://help.metaphacts.com/resource/Help:VisualOntologyEditing

9. •Title (dcterms:title)
•Label (rdfs:label)
•Description (dcterms:description)
•Base Element Namespace (IRI)
•Version info (owl:versionInfo)
•Version IRI (owl:versionIRI)
•Created (dcterms:created)
•Creator (dcterms:creator)
•Contributor (dcterms:contributor)
•Imported ontologies (owl:imports)
Ontology MetadataManagement

10. •Access to other ontologies in the catalog for modularization
•Ontology elements can be referenced for reuse, integration and alignment without
changing their definition or ownership
Networked Ontologies

11. •Import and versioning through Git
Ontology Management in Git

12. •Import and versioning through Git
Ontology Management in Git

13. •Change Detection and Git Versioning
Ontology Management

14. Model-driven Development
Model-driven Forms
Data Validation

15. Get Started –NOW
Proof of Concept
1-2 weeks
MVP
3-4 weeks
Production
1-2 months
Experience data in context
Deliver meaningful and actionable insights
Empower end users
Adapt as you go
Drive digital transformation