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.”
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.
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!
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.
As we move to data-centric AI, we demand higher quality data. Yet, the first step in creating predictive models odten 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.
The job of a database adminstrator 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 you 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.
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.
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.
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.
This was the 10th ICLR conference, marking the golden decade of deep learning and AI. 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. Here we present what we found to be the most noteworthy ideas.
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.
Learn how the RelationalAI team was able to work with a big 4 tax agnecy to capture complicated application logic with 50 times less code. Find out how they accelerated calculations up to 10 times faster.
What modern graph platforms have learned from SQL and relational databases.
Our team's highlights of The International Conference on Learning Representations (ICLR) in 2021.
Molham and Ellie talk about why relational always wins and the impact on graph technology.
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.
RISElab Seminar 09/25/20 - Molham Aref provides an overview of the relational paradigm and why enterprise-grade Knowledge graphs should leverage the same approach.
At RelationalAI, we believe relational knowledge graphs are the foundation for future data-centric systems, and we are excited to demo the reactive notebook environment we built for working with knowledge graphs here with you!
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