This blog will speculate on how enterprise knowledge graphs (EKGs) will evolve to contain specialized functions and specialized subgraphs. We will use metaphors from the evolution of centralized nervous systems (CNS) in primitive life forms to make some key points about architectural trade-off analysis.

If EKGs are really going to become the centralized “Brain” of organizations, they will need to evolve from where they are today to take on new roles. We also need to understand the challenges of depending on central control of organizations.

EKG and CNS Metaphor: Sponges, Octopuses, and the Human Brain

We will be using the metaphor of the CNS in organisms to discuss how EKGs are evolving. This requires us to look at the spectrum of different ways that organisms have evolved different types of nervous systems and how important these are at adapting to new environments. …

This is my third annual post on Enterprise Knowledge Graph (EKG) trends. You can also find my 2019 and 2020 posts on this blog, and I think you will find several consistent patterns in these three posts.

Graph Database Continue to Grow in Popularity

Interest continues to grow in EKGs. We can see from the DB-Engines popularity change chart below that Graph Databases still outpace interest growth of all other database types by a wide margin.

Should you store business rules in your Enterprise Knowledge Graph (EKG)? This is a non-trivial question, and how you answer it might change your enterprise knowledge graph strategy.

A few weeks ago, I wrote an article on the incredible new Intel PIUMA chip architecture and how it will change the face of computing by offering a 1,000x improvement in knowledge graph traversal performance. These chips could be manufactured at a low-cost and integrated into many devices and portend many changes to the computing industry and software design. This is not just the end of the relational database era. …

In the last year, graph embeddings have become increasingly important in Enterprise Knowledge Graph (EKG) strategy. Graph embeddings will soon become the de facto way to quickly find similar items in large billion-vertex EKGs. And as we have discussed in our prior articles, real-time similarity calculations are critical to many areas such as recommendation, next best action, and cohort building.

The goal of this article is to give you an intuitive feeling for what graph embeddings are and how they are used so you can decide if these are right for your EKG project. For those of you with a bit of data science background, we will also touch a bit on how they are calculated. For the most part, we will be using storytelling and metaphors to explain these concepts. We hope you can use these stories to explain graph embeddings to your non-technical peers in a fun and memorable way. …

For the last few years, I have been promoting the idea of the Hardware Graph. My assertion was that graph hardware needs a focus on simple pointer hopping at scale. I have always stated is that the best way to do this is to use full-custom ASIC chips and memory designed for random memory access that supports pointer hopping over large memory footprints. Although I had privileged access to some insider knowledge of developments in this field, on October 13th, 2020, Intel published the results of their groundbreaking research on how they are building the next generation graph hardware. Now that this paper is in the public domain, we can openly discuss this new architecture and its impact on the Enterprise Knowledge Graph (EKG) industry. …

On November 5th I attended and presented at the Midwest Architecture Community Collaboration (virtual) conference. My topic was on why graph embedding will become a ubiquitous feature of knowledge graphs. But that is not what this blog is about. This post is about the topic of how Enterprise Knowledge Graphs (EKGs) can promote more adaptable enterprise architectures through taking a Systems Thinking approach to enterprise IT strategy.

The keynote speaker was George S. Paras, the Managing Director of EA Directions and Editor-in-Chief of Architecture & Governance. He is a well-regarded enterprise architect. Although I would not classify him as a graph evangelist (yet), he really knows his systems thinking. …

How EKGs and Systems Thinking can make the world better

Recent developments in distributed graph database technology have created a Cambrian Explosion in the capabilities of the world’s high-tech companies. Innovations that were once isolated to Bay Area companies like Google, Facebook, LinkedIn, and Twitter have migrated into commercial products like TigerGraph. These enterprise-scale graphs have enabled many companies to use these revolutionary technologies to provide integrated views of their customers and deep insights that save hundreds of millions of dollars a year.

However, making companies more efficient is not the only topic of interest to me. What is most striking is how these new integrated views of knowledge are changing the way we think. The new capabilities that Enterprise Knowlege Graphs have enabled are started to rewire our brains. They are triggering new connections in our brains that have never existed. They are a form of neurogenesis. And the patterns we are learning within the knowledge graph domain are being applied to other domains outside IT. This is the true revolution that is happening in organizations that are building enterprise knowledge graphs. …

It is complex and expensive to extract data from a database, send it to a GPU, train a model, and then use this model to enrich a database. What if we could leave our data “in place” and continuously run algorithms that would automatically enrich our database with new insights? This is the vision behind a new generation of systems called “In Situ” machine learning systems. They reflect a new trend to integrate machine learning directly into our enterprise knowledge graphs.

The term In Situ means “in the original place”. In this context, it implies that we will keep data in place in our enterprise graph, and we are going to design our systems to minimize the need to move data around. If we look at the problem from the Systems Thinking perspective, we realize that the reason that we started moving data around is that older databases were incredibly inefficient at traversing relationships and binding specific datatypes to computational resources. This is because older relational databases were designed to run on a single server. Before the arrival of Massively Parallel Processing (MPP) enterprise knowledge graphs, the design of the older relational databases makes it difficult to do analysis over a cluster of 100s of nodes. With the arrival of Graphcore and the new Intel PIUMA architectures, we need to start thinking of our databases as integrated data-compute resources distributed over many servers and even sometimes in different data centers. It is now our task to rethink every process in our enterprise knowledge graph that requires unnecessary data movement. …