Solutions
Neighborhood-based Recommender Systems in Rel
Recommender systems are one of the most successful and widely used applications of machine learning. Their use cases span a range of industry sectors such as e-commerce, entertainment, and social media. In this post, we focus on a fundamental and effective classical approach to recommender systems, which is neighborhood-based.
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Knowledge Graphs for Earthquake Data
Rel and the Relational Knowledge Graph Management System provide an excellent tool for investigating and analyzing seismic data. This project illustrates an example of working with data that is distributed geographically and temporally.
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Trick-Or-Treating With Rel
Here at RelationalAI, we're passing out knowledge graphs to trick-or-treaters. Come and get spooked with us as we solve a Halloween logic puzzle in Rel, our relational modeling language. You'll see how to model a problem, store facts, and infer new knowledge from those facts. So grab your flashlight, put on your favorite costume, and let's go trick-or-treating with Rel!
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Declarative Quantum Computing
All businesses regularly confront difficult decisions. Even when these decisions are constrained (perhaps by budgets) and the cost implications of the decisions are complex, optimization algorithms may nevertheless provide decisions that minimize cost and maximize benefit. Unfortunately, optimization can become very difficult as the number of decisions increases and this makes it an excellent use-case for quantum computing. Declarative languages like RelationalAI’s Rel make the integration of quantum optimizers simple.
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Machine Learning in Consumer Credit: A Knowledge-Driven Approach to Mitigating Bias
The financial services sector was one of the first industries to widely adopt predictive modeling, starting with Bayesian statistics in the 1960s and evolving after the advent of neural networks to deep learning and beyond. Machine learning applications in this industry are endless, whether in auditing, fraud detection, credit scoring, or others.
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Understanding Our World
Rel is not a procedural language. It is a relational declarative language with roots in logic programming. Rel goes far beyond a simple type system by allowing you to define the rules your creations have to obey. Definitions are not methods that turn input into output, but rather rules that can be evaluated in both directions.
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Good Neighbors
Rel supports relations as arguments, recursion, variable arguments and inlined definitions that reference other inlined definitions. These features give you tremendous power so you can express your business logic clearly and concisely. Power that was typically reserved for procedural languages is now available in your models and queries.
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Playing With Legos
Do you remember playing with legos as a kid? This is what our query language Rel feels like. You don’t have to write 300 lines of SQL code and go absolutely insane trying to debug or handle it all in your head. You make progress with every little definition. You can divide and conquer the problem by building small pieces, combining them together and creating something that is greater than the sum of its parts.
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Tic-Tac-Toe: Knowledge Extraction From Imperative Code
Applications implemented in imperative programming languages encode vast amounts of human knowledge at their core, but the essence of the application logic is often obscured by the imperative nature of the implementation language.
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Modeling an Agent-Based Simulation in a Relational Database
We show how the relational model can be successfully exploited to model complex analytics scenarios while enjoying the same characteristics of clarity and flexibility as when modeling the data themselves.
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AI-Enhanced Customer Experiences with Relational Knowledge Graphs
A Fortune 50 retailer drove $1 billion in incremental revenues over the last three years after deploying AI models developed by RelationalAl.
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Metadata Management Series: Compliance Reasoning with Knowledge Graphs
Demonstrating the details of weaving metadata into a knowledge graph to automate checking and maintenance of complex policy requirements.
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Metadata Management Series: Weaving Asserted and Discovered Metadata into Your Data Fabric
Showing how knowledge graphs support the construction of scalable models that mix discoverable with explicitly asserted metadata to afford reasoning and policy enforcement. In this first article, we show how explicitly asserted metadata in a knowledge graph enables automated reasoning.
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Losing the Middle Tier
Business applications are traditionally complicated to understand and maintain because business logic tends to be scattered in separate tiers of growing code bases, while schema becomes rigid and brittle. The end result is that business logic dissolves in a soup of disparate application code.
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Accelerating AI Adoption with Explainability in SaMD
Many recognize the potential of artificial intelligence (AI) in healthcare and life sciences. Some early adopters are starting to reap the benefits already, especially in fields such as radiology and digital pathology.
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RAI Platform Workload Scenarios
The RAI relational KGMS combines Graph, Reasoning, and Learning in a single platform. Add cloud scalability, workload concurrency, and zero-copy cloning to get unique analytic flexibility.
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