Written by Sebastian Schmidt on . Posted in Ontology Modeling
In my previous blog post on building Knowledge Graph-driven, FAIR Data platforms I discussed the importance of data and data-driven decisions, processes and tools in accelerating digital transformation. Knowledge Graphs have revolutionized the way data can be accessed and used, and have helped enterprises overcome the challenges posed by distributed silos where information is available to limited audiences, in heterogeneous formats, and represented according to different models. They have led to great advances in terms of data integration, interoperability and accessibility, and have allowed companies to tap into the full potential of their data assets and transform data into valuable and actionable knowledge.
With metaphactory, our customers have been able to rapidly build Knowledge Graph-based applications enabling them to focus on business outcomes, reduce development efforts and quickly produce results that matter:
- Customers in Life Sciences & Pharma have been able to fast-track drug development and drug repurposing.
- Customers in Engineering & Manufacturing have established smart manufacturing processes and have sped up research, documentation processes and industrial configuration management.
- Customers in Government and Cultural Heritage organizations have streamlined data curation and digital publishing processes, making cultural heritage content intuitively available to the public.
All of these applications utilize a semantic data model to not only describe the domain, but also drive data integration, tie in term vocabularies, or derive UI templates to create a model-driven user interface. Such a semantic data model is called an ontology. According to Gartner, "Ontologies are structural frameworks for organizing information and are used as knowledge representation. Ontology management supports and expands data modeling methodologies to exploit the business value locked up in information silos."