Many aspects of data management—particularly concerning big data—hinge upon the utility of graph databases. When deployed with additional semantic technologies such as ontologies, taxonomies and vocabularies, there are few analytic feats an RDF graph cannot achieve. In most instances, end users are largely unaware of the degree of complexity that semantic graphs account for when linking and contextualizing disparate data elements for unified results.
Graph databases initially gained prominence with use cases involving social media and facets of sentiment analysis; this technology gained credence by provisioning ‘360 degree views’ of customer and product information in MDM systems. Other commonly found uses of graph databases include applications of time-sensitive data such as recommender engines for e-commerce, fraud detection for finance, search engine augmentation, and ERP optimization.
But as valuable and as proven as these individual uses cases are they only hint at, and do not truly attest to, the full array of possibilities of databases powered by semantic graphs.
Today,semantic graphs are greatly expanding the utility of data lakes in a sustainable manner. The enhanced analytic capabilities of semantic graphs are integral to cognitive computing analytics, as well as to analysis of integrated unstructured and structured data.
The true potential of semantic graphs is realized in linking the entire information assets of an organization for comprehensive analytics of overall internal data as well as public data and third-party data. The underlying architecture for such an undertaking commonly involves Hadoop or some other other data lake to account for issues of scale; ontologies are required for a semantically consistent model, and terminology systems are needed to clarify terms and definitions.
Once this architecture is in place, an RDF graph links the data via the semantic model to distinguish points of relevance between all data throughout the enterprise. Laymen end users in the business can access all of their company’s data when performing analytics.
Best of all, semantic graphs are responsible for denoting just how a particular node relates to another node to inform analytics with a critical element of context. End users need not understand those relationships themselves to benefit from the propensity of semantic graphs to link and identify relationships between any type of datain a comprehensive semantic model.
The comprehensive nature of semantic graph-based analytics partly pertains to its incorporation of structured data alongside unstructured and semi-structured data.
Once sources are loaded, semantic graphs enable expedient data integration based on their ability to link different data elements.
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