Comprehending semantic technology is no longer an arduous task for the back offices of data-savvy organizations. Business users and C-level executives are starting to comprehend the basics of the technologies that are increasingly impacting their jobs.
The term “semantics” signifies “meaning”. Semantic technologies provide a clarification of meaning across data elements based on the open standards of the World Wide Web Consortium (W3C), which readily standardizes data regardless of source, structure, or use case.
Semantically tagged data are called smart data due to their self-describing nature. All data are described via semantic statements known as triples, which contain a subject, verb, and object. Triples function as the foundation of semantic technologies and are greatly responsible for their ability to understand, link, and contextualize data more quickly, and with less effort required, than others approaches can.
Semantic models are ontologies that standardize the schema for which to model data. The benefit of using W3C sanctioned ontologies such as OWL is that they include all data types. Semantic models are more pliable than typical relational models because they swiftly expand to include additional data sources and structures, changing requirements, and any assortment of modeling logic including conceptual, business, and underlying IT system logic. Almost any facet of data can be modeled, including governance or regulatory mandates. Ontologies offer a uniform way to align data in an inclusive manner that doesn’t require extensive recalibration time; they are ideal for integration, can automate code for transformation, and are an astute precursor for business intelligence and analytics.
Semantic graphs are triple stores, are standardized via the Resource Description Framework (RDF), and enable the linking of all data. RDF graph databases are unlike non-semantic graph databases because they focus on the relationships between data as opposed to the data (nodes) themselves. Emphasizing relationships is critical to contextualizing seemingly unrelated data and drastically improves analytics by determining how all data correlate to a specific question. This profound understanding between data characteristics delivers insights that would otherwise elude users, vastly enriching the efficacy of analytics.
Vocabularies and Taxonomies
Semantic technologies are based on triples, which are modeled in standardized ontologies. RDF graph databases allow all data to be linked together for contextualized understanding between elements. Vocabularies and technologies are additional semantic tools that help clarify the meaning of terms in a standardized format. Semantic standards apply across all data and IT systems.
To learn more about Semantic Graph Databases, watch our on-demand webinar “Semantic Graph Databases: The Evolution of Relational Databases“.