We’ve integrated machine learning in Stardog 5. And now you’ll learn the rest of the story.
TLDR: Better data means better insight, faster. Machine Learning with access to all the data means better insight, faster.
If you want to know how machine learning (ML) in Stardog 5 works, read Learning to Predict. But if you want to know why we integrated ML into Stardog read on.
The real question is how does ML benefit Stardog’s customers and users? To answer that let’s consider the two main patterns of AI-enabled startup:
In short, we think that (1) are all doomed and (2) is where the wins will happen. Type 1 startups have no economic moat and no chance to build one. Type 2 startups do.
Stardog is a Type 2 startup. We are a full-service stack and the strategic business problem we’re devoted to solving is the problem of enterprise data silos. And machine learning will help us get there faster.
All enterprise IT—and, in fact, most all of enterprise period—will in the future run on data; and that will enable Type 2 startups to thrive. Stardog is purpose-built to enable all enterprise activities, including other startups, to more fully monetize data as the strategic asset by solving the enterprise data silo problem.
How will putting ML into Stardog help us our customers do that? Two ways: building the knowledge graph; and deriving actionable insight from it.
The three biggest knowledge graphs—Google, Facebook, LinkedIn—on the planet are built with, in part, machine learning (ML) techniques. Why? Because these knowledge graphs are made from other people’s data—the web itself, personal life, professional life, respectively. Those people and orgs, at least in principle, own their data and can change, republish, improve, degrade, shorten, or lengthen their data whenever and as often as they want.
So Google-Facebook-LinkedIn use automated measures to respond appropriately to that unending and non-negotiable flow of changes. The reason knowledge engineering done by really smart people with good tools isn’t the primary approach any more is the flow of data is too fast, too large, and too diverse. Knowledge Graph creation is (partially) automated because there is no other way.
While the data landscape “behind the firewall” is quite different in some respects from what Google faces on the public Web, there is also a lot of overlap. We know that many of those ML techniques the Big 3 employ work in the enterprise, with suitable modifications.
So ML helps us get to the Enterprise Knowledge Graph faster. And then that superior data accessibility creates a virtuous cycle between greater data unification and better actionable insight based on what the organization in sum knows. The thing about an enterprise knowledge graph is that it should know stuff. And knowing stuff is a lot like learning stuff. People who know and learn stuff are good. Machines that know and learn stuff are good, too. The combination of people and machines are the best in part because the combination of better (i.e., all) the data and the right algorithm is the best thing of all.
Knowledge graphs know stuff; your enterprise knowledge graph should know stuff about your enterprise. And it should learn more stuff from the stuff that it already knows. That’s where we’re headed. Stay tuned for more ML and more AI in Stardog:
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|Core Services||25M Nodes & Edges|
|Complete OWL 2 Reasoning||10K Axioms|
|Integrity Constraint Validation||20 Constraints|
|Enterprise Graph Security & Auth||4 Users/Roles|
|Amazon Web Services|
|Pivotal Cloud Foundry|
|Permissable Use||Non-commercial||Commercial production|