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Industry Insights

How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications: Research by Gartner®

The Gartner report, How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications, published in September 2022, states that “knowledge graphs deliver semantically enabled data management to power a diverse range of AI applications.”

What’s So Special About Graph Analytics?

Graph analytics help us make sense of our connected data by understanding the structure of our data. They help us see which patterns are important and which aren't. They help us predict what’s coming next. And they help us find control points so that we can be prescriptive and enact change.

Parsing the Crowded World of Data Analytics: Highlights

Our board member Bob Muglia recently met with Sanjeev Mohan in an interview for the It Depends podcast. Bob and Sanjeev discussed the challenges, trends, technologies and the general pulse of the ever-changing data analytics market. Here are some highlights from their discussion!

Trends in Machine Learning: ICML 2022

The 38th International Conference on Machine Learning (ICML) took place in mid-July in hybrid mode, two months after the International Conference on Learning Representations (ICLR) 2022. In our analysis of the ICLR, we pointed out the spread of language models (LMs) and self-supervision. Things are not that different at ICML. Read our analysis.

Stop Wasting Good Data: Reclaim Predictive Information with Knowledge Graphs

As we move to data-centric AI, we demand higher quality data. Yet, the first step in creating predictive models often involves tossing out predictive information. In this post, we’ll look at how data scientists and ML engineers can use a relational knowledge graph to make domain expertise and contextual information available to ML models. Learn how to stop wasting the predictive data you already have.

Is Database Administrator Still a Good Job?

The job of a database administrator is evolving, just as almost all careers in IT. DBAs are often the ones closest to the task of capturing the real world. This is arguably the base of the entire application stack. If your model is wrong, your data will be wrong, your predictions will be wrong, your decisions will be wrong. It all falls apart without the right model. It falls apart without those people who know what to capture and in what detail, what to connect and in what ways. That’s the DBA.

Loopy Lattices and Recursion

Our semantic optimizer is a query optimizer that uses the knowledge of user constraints, the knowledge of the data at hand, and the knowledge of mathematics to find an algorithm to answer a query in the fastest way it can. Here we touch upon just one feature of the semantic optimizer: recursion.

Loopy Lattices

About a decade ago, Marko Rodriguez wrote a blog post on Loopy Lattices. It became infamous amongst graph database practitioners as it taught them a very important lesson: Never give in to the temptation to ask for all potential paths between two nodes.

(RAI)volutionizing Master Data

Most master data projects fail because we’ve relied on monolithic MDM platforms, and therefore centralized, highly federated approaches to creating master data. The MDM concept is strong. The benefits and uses of master data can provide value to those corporations who successfully implement it. However, we need to fundamentally change our approach to implementation.

Trends in Machine Learning: ICLR 2022

Despite early predictions that the deep learning hype would be ephemeral, we are happy to see the field still growing while delivering maturity in algorithms and architectures. ICLR 2022 was full of exciting papers. Here at RelationalAI we spent ~100 hours going through the content as we believe it will drive the commercialization of AI in the following years. We present what we found to be the most noteworthy ideas.

KGC Highlights: The Relational Approach to Graphs is Here to Stay

We recently participated in the Knowledge Graph Conference (KGC) in New York, which included educational workshops, guides to getting started, and expert thought leadership in knowledge management and graphs.

The False Dichotomy of ‘Graph vs. Relational’

What modern graph platforms have learned from SQL and relational databases.

Case Study: Big 4 Tax Accountancy Sees 10X Speedup

Read how the RelationalAI team was able to work with a big 4 tax agency to capture complicated application logic with 50 times less code. Find out how they accelerated calculations up to 10 times faster.

Trends and Progress in Artificial Intelligence from ICLR 2021: Language Models & Data Programming

Our team's highlights of The International Conference on Learning Representations (ICLR) in 2021

Retail Recovery - Thriving in the New Normal

Mark Pilkington discusses the future of retail, providing fascinating insight into how retailers can recover and thrive in retailing's "new normal."

RelationalAl & Julia

At JuliaCon 2021 Huda Nassar gives an overview of what we do at RelationalAI, and highlights how we've used Julia to develop our RKGMS and modeling language Rel.

Vectorized Query Evaluation in Julia

RelationalAI is using Julia to build a next-generation knowledge graph database that combines reasoning and learning to solve problems that have historically been intractable. Molham and Nathan explain how Julia's unique features enabled them to build a high-performance database with less time and effort.

Industry Insights