Introduction to the Relational Knowledge Graph System
At RelationalAI, we have built the first Relational Knowledge Graph System (RKGS).
We believe relational knowledge graphs are the foundation for data-centric applications. These are systems that learn, reason, and predict over richly interconnected data. With a Relational Knowledge Graph System as the foundation, diverse data application workloads can either be learned or declared, all grounded on the same data.
A Relational Knowledge Graph System as the foundation for these data applications minimises accidental complexity and automatically provides performance, scalability, incrementality, provenance, and audit trail. Accidental complexity arises in legacy applications because the business can’t read and understand the code written by the developer and the developer doesn’t have the domain knowledge of the business SME. We end up with a vast number of interrelated and interconnected applications running our enterprise that no one person or even department can fully understand or explain how the pieces and parts function across the enterprise.
Our observation is that individual systems usually have 10 times as much accidental complexity as essential and that systems of systems (the vast interrelated set of applications that run most enterprises) have ratios that are 100 to 1000 times as much accidental complexity as essential. Eliminating accidental complexity is the most important thing most firms can do.
Excerpt From: Dave McComb. Software Wasteland: How the Application-Centric Mindset is Hobbling our Enterprises.
Our approach opens direct two-way communication between the domain expert and the developers, so the solution is more likely to match the requirements and be completed in a shorter timeframe with a smaller enterprise footprint.
Data Applications built on the RelationalAI platform operate directly against the relational structure. For maximum scalability and flexibility, RAI’s relational structure is fully normalized. Queries against fully normalized data would perform poorly on a typical database, which performs n-way joins as a series of 2-way joins, generating large amounts of intermediate results. But RelationalAI has implemented new join technology and algorithms that makes this feasible. For more information on this visit our research page here.
Machine Learning / AI and legacy imperatively coded applications are Black Boxes. The models and code are created by specialists, but are opaque to business people. This leads to lack of trust in the AI results. Our approach dramatically reduces lines of codes and declares the requirements in a more concise human readable format. These make the solutions and the results more explainable, easing the path to production and adoption by the business.
The days of people telling the computer step by step how to perform a task are behind us. We no longer have to describe how we want to win a game, we just declare the rules of the game and that we want to win it. The platform reasons out the rest.
RelationalAI is a cloud-based relational knowledge graph management system, with state of the art probabilistic processing and declarative reasoning at scale to make developing Data Applications a superpower for your business.
Our modeling language lets developers declaratively model their business problem, define and enrich an enterprise scale Knowledge Graph from multiple data sources and with computed knowledge, then query and reason over the graph. Think of our language as “executable specifications”, enabling high-bandwidth communication between domain experts and application developers.
Business modeling is not new. But combining it with Knowledge Graphs built on a relational architecture, and hosted in the cloud, IS new. We believe it is the inevitable next step in the evolution and expansion of the relational paradigm and how data applications will be built.
Most application-centric data models use some variation of 3NF, with wide tables designed to answer an application’s specific typical queries. If a different application’s workload needs a different schema, the data is copied and transformed to suit that second application. Data duplication and silos are the result and business-level constraints, the rules that define the integrity and meaning of the data, are implemented in application-specific code.
If your schema is not organized for a specific application workload, it can better adapt to unanticipated workloads. A business can answer new questions without having to copy and reorganize the data. Whereas the application-centric approach leads to duplication and silos, our approach leads to treating a dataset as an organization-wide asset.
Existing machine learning algorithms operate against wide data frames, or tensors, or matrices, depending on your terminology. These data frames are extracted and labeled in the feature engineering process. This consumes both compute and human processing time.
RelationalAI Knowledge Graph Management System includes fundamental innovations in this field. We are tapping into almost 2 decades of profound and under-utilized research by the database theory and algorithms community… . . more on this in other posts and on our research pages.
Thank you for reading this post introducing RelationalAI, if you would like to learn more please reach out via email or through the contact us section of the website.
In some of our other posts we discuss where we are today and where we will be in the future. In this post we look at the historical trends that we have witnessed that support our belief in the Relational paradigm and that a relational knowledge graph management system is history repeating itself.
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