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Hard to find Data is hard to find for everyone.

The Semantic Knowledge Graph database provides a mechanism for the creation, curation and management of all records. The same database can also drive Sales, Marketing, and coordinated SEO operations by converting critical product and company data into streams that Google, Microsoft Bing, Amazon, other search engine and industry aggregator sites can use as authoritative information. During this entire process you still maintain control of the underlying data. SDS Technology optimizes internal information in order to democratize data, extract and enable insights for customers, while building tools to increase engagement.

The Semantic Data Solutions Knowledge Graph Database

Knowledge Graph databases are the fastest growing category of data technology today. It’s definitely due to their efficiency, as well as ease of use for both experienced and non-IT literate staff. The development team at Semantic Data Solutions began working on the KG Database technology with one of the largest supply chains in the US, proving efficiency and a huge savings of resources. The market has embraced the technology, Airbnb, eBay, Amazon, FinTech, Salesforce, Uber, Boeing among many others are beginning to implement the technology. Its success can be attributed to many factors, but the main reason is simply converting vast amounts of complex information into a more simplified version which then creates intuitive relations that are easy to read and represent visually.

Considering databases are a living organism, always growing, and containing more times than not, difficult to comprehend and interpret information; the Semantic Data Solutions Knowledge Graph database transforms that data into easy to work on, ever evolving, and traversable data that can be interactive with other teams.

The SDS Knowledge Graph databases can succeed where others databases won’t, especially in scenarios that involve leveraging more detailed information, connections and powerful semantics. Data silos are disconnected from everything! They prevent larger structures from easily being created and are responsible for slowing most projects: app dev, data science, analytics, reporting, compliance, and the machine reading robots of our future dreams.

Data silos mean undemocratized and therefore unconnected data—and unconnected data sucks. SDS Knowledge Graphs mean democratized data, connected to all the other knowledge graphs, when applicable, ready for Smart Contracts and AI.

“Using graphs to not only describe their operation but to know about your customers 360 [degrees]…describing all the things you know about your customers and what other connections they have.

“[By] merging multiple sets of data and then performing complex analytics powered by the real-time operational [graph] analytics.”

Combining data from multiple sources

To make decisions, businesses combine databases with non-proprietary data, combining diverse data from numerous sources. This is really complex. SDS KG Technology builds Big Knowledge Graphs, quickly, applying cognitive analytics enabling entity awareness across several industries.

“Back in June 2016, Brexit was predicted based on an analysis of a million tweets…based only on impressions, interactions and influencers…the data made it clear the Brexit support was at least <100%.” Our CTO Kurt Cagle, a Forbes contributor and longtime Knowledge Graph practitioner, explains this technology can be leveraged to predict other outcomes as well. “The key lies in the ability to combine quantitative and statistical bottom-up analysis with qualitative analysis based on expert-provided knowledge. “Although this technology has been available for a long time, developing an automated process easily managed and scaling it up quickly has been the key challenge to its adoption. We can now introduce our technology without disrupting your current data system. Upon the early adopters of any technology fall the burdens and benefits of proofing, exploring, and expanding applications. Today, the likes of Airbnb, eBay, Salesforce, Uber, we can say 2018 was the year knowledge graphs went mainstream.”


Semantic Knowledge Graph databases give us the power to represent a problem domain using a graph, and then persist and query that graph at runtime. We can use graphs to clearly describe a problem domain; graph databases then allow us to store this representation in a way that maintains high affinity between the domain and the data. Further, SDS knowledge graph modeling gets rid of the need to normalize and denormalize data using complex data code.

Many of us, however, will be new to modeling with graphs. The graphs we create should read well for queries, while avoiding conflating entities and actions—bad practices that can lose useful domain knowledge. Although there are no absolute rights or wrongs to graph modeling, SDS provides the guidance to help you create graph data that can serve your systems’ needs over many iterations, all the while keeping pace with code evolution.

With the knowledge of graph data modeling in your grasp, your teams can now begin working together on graph database projects.

We help to solve the problem without special complications.