The terms "ontology" and "vocabulary" are often used interchangeably. However, more often than not, this leads to confusion among customers who want to semantically model their domain and results in questions about whether there is in fact a distinction between the two and whether both are needed to implement a knowledge graph.
The meta-layers that these terms describe have been captured by different standards (OWL and SKOS respectively) and we at metaphacts believe that there is value in treating both as individual but complementary assets in their own right.
This blog post introduces metaphactory's vocabulary management features, which extend the platform's knowledge modeling capabilities and support knowledge graph experts, domain experts and business users in creating and editing SKOS vocabularies to capture business-relevant terms. We'll start out by defining what vocabularies are and looking at the use cases they can serve. Then, we'll look at specific vocabulary management features supported in metapahctory. Finally, we'll look at a specific use case and integrate an existing thesaurus into metaphactory and use the platform's semantic structured search component to explore terms and to connect data through relations between entities.
Timely access to consumable, contextual, and actionable knowledge is crucial for any step in the decision-making process and the key enabler of decision intelligence. However, decision makers and decision support systems are still faced with the everlasting challenge that data relevant to and required for addressing their specific information needs is stored in distributed and database- or application-specific silos.
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."
