Graph Triumphalism

The Next Battle Ground for the Titans of Tech by Charles Silver.

From the post:

To win this galactic battle for dominance of Web 3.0, the victorious titan must find a way to move the entire tech world off of relational databases — which have been the foundation of computing since the 1970s — and onto graph databases, the key to semantic computing. The reason: Relational databases, though revolutionary way back when, are not up to the job of managing today’s Big Data. There are two huge, insurmountable issues preventing this:

  • Data integration. Relational databases (basically, all that stuff in silos) are finicky. They come in many forms, from many sources, and don’t play well with others. While search engines can find data containing specific keywords, they can’t do much of anything with it.
  • Intelligent “thinking.” While it’s impossible for computers to reason or form concepts using relational databases, they can do exactly that with linked data in graph databases. Semantic search engines can connect related data, forming a big picture out of small pieces, Star Trek-like.

This is exactly what users want and need. Consumers, marketers, advertisers, researchers, defense experts, financiers, medical researchers, astrophysicists, everyone who uses search engines (that’s everyone) wants to type in questions and get clear, accurate, complete answers, fast, that relate to them. If they’re shopping (for insurance, red shoes, DIY drones), they want where-to-get-it resources, ratings and more. Quite a wish list. Yet chunks of it are already happening.

I really like graph databases. I really do.

But to say relational databases = silos, with the implication that graph databases != silos, is just wrong.

Relational or graph databases (or any other kind of information system) will look like a silo if you don’t know the semantics of its structure and the data inside.

Technology doesn’t make silos, users who don’t disclose/document the semantics of data structure and data create silos.

Some technologies make it easier to disclose semantics than others but it is always a users choice that is responsible for the creation of a data silo.

And no, graphs don’t make it possible for computers to “…reason or form concepts….” That’s just silly.

Law of Conservation of Intelligence: You can’t obtain more intelligence from an system than was designed into it.

PS: I know, I’m cheating because I did not define “intelligence.” At least I am aware I didn’t define it. 😉

2 Responses to “Graph Triumphalism”

  1. clemp says:

    And no, graphs don’t make it possible for computers to “…reason or form concepts….” That’s just silly.

    Why is that silly? Maybe it is just a matter of scale! The physical graph in all our heads allows us to reason and form concepts. Why should a computer be any different once we get to the right scale.

    A human brain contains around 100 billion neurons (graph nodes). No problem there but each neuron connects to up to 10,000 other neurons (relationships). Hmm, current graph databases might be a little shy of that. When we get there, and computers are programmed to support nodes that form relationships on the fly based on new data, why couldn’t a graph database form a concept?

    In “On Intelligence”, Jeff Hawkins makes a compelling case for intelligence and the ability for abstraction arising from hierarchical organization of “data” stored in the connection strength of neurons. The “concept” just being the persistent structure that remains constant across a wide range of data input.

  2. Patrick Durusau says:

    Sorry for the delay!

    “Silly” because until recently the Neurogila or simply gila cells were thought to just be packing for neurons. That about half the brain by mass. Moreover, there are complex connections between the gila and neurons, including using biochemical processes.

    The problem being we don’t have a clear picture of how the mind works, much less a clue on how to imitate how it works.

    Even George Boole, in An Investigation of the Laws of Thought,, pp. 327-328, concedes that boolean logic isn’t “thinking” in many important ways, perhaps the ones that matter the most.

    A graph is a simplified model that is amenable to computer processing. When we write a graph we are creating a simplified model, not a replica of how we thought about some subject. That why I think saying that graphs are going to enable computers “to reason or form concepts… is just silly.

    Hard to imagine the gap between a simplified model and the actual process of thinking, which can “…reason and form concepts….”