alt Hacker NewsNvidia – open-source auto-ontology
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This may well be very useful, but Claude has ruined me and I have a visceral reaction to ‘gates’ in the opening sentence and couldn’t get any further. If a tool is worth building and sharing, surely it’s also worth writing a brief summary of what it’s supposed to achieve in your own words?
I want to attempt an explanation as it seems a lot of folks here didn't grok the very terribly written README
"Auto Ontology" is sort of a poor name.
Here's the problem:
Your company has data scattered across multiple types of databases. People who make decisions want to ask questions about that data.
The old way is they would go to a Data Analyst, and say, "Get me a report on so and so", and it was the Analyst's job to iron out the right series of queries and one-off scripts to generate the needed info.
With a system like this, the idea is that all your database models have been introspected and stored in a central metadata catalogue.
When a question is asked like "What were the top selling items in the month of May?", the metadata catalogue tells you "Products are stored in FOO_DB" and "Product_Sales are stored in BAR DB" and so you go and fetch the right data from the right place and do whatever in-memory calculations you need to do.
What the system provides is the implementation code for:
- Introspecting each of your DB's
- Aggregating model + metadata information in a central, virtual DB (VDB)
- Generating dialect-specific SQL from natural language queries and routing them to the right DB for the underlying table
- Combining results in-memory when a natural language query requires multiple datasources
- Handling proper permissions, etc
A GOOD system for doing this also integrates with your external API sources (OpenAPI, GraphQL, gRPC, whatever) and also documentation sources (cough, I work on this, cough).
Fixing unreliable agents by adding more unreliable agents. It's a shame we can't do something about the tendency of LLMs to give more weight to their weights than to the data they're analysing.
I guess it all comes back to the fact that they don't have a clear division between data they have learned about the world, instructions and data under analysis.
You can ask them only to consider the 5 facts in front of them, but they won't do that, and cannot do that.
I’m struggling to see why this is better than OWL/RDF and SparQL. Given the breadth and depth of semantic web knowledge in the corpus they do an amazing job discovering well established ontologies and building new ones on presented data and querying it with sparql. In fact better than almost anything I’ve seen. What an irony that the semantic web after all these years is likely to find a real use - it’s original one in fact.
I've spent a career working with data and ontologies and I couldn't understand this readme, and the diagram only makes it more confusing.
I still prefer my own, simple and more functional CAVE lang [0]
I have a question for people with knowledge of both LLMs and GOFAI: Why can’t we use LLMs to write as many symbolic rules as needed to fit the data while keeping the ruleset reviewable and maintainable?
I always giggle when software engineers use the term "ontology."
Was interested... but then realized it needs an NVIDIA api key?
We spend a small fortune trying to get ontology right as it's so critical to real world success of enterprise AI. Curious if this could actually help us.
I tapped out at the AI-isms in the readme.
Nvidia, I get you're all-in on AI, but please at least find a human to write some copy. It's both overly verbose and entirely lacking of important info at the same time.
I don't understand this ontology thing. What's the difference between this and a memory system plus skills to collect your enterprise's APIs and CLIs etc?
Open Source?
How about we start by open-sourcing the NVIDIA user-space libraries: libcuda, libcudart, libOpenCL, libnvtx, as well as nvidia-smi and the CUDA and OpenCL profilers.
Didn't expect Standard ML O_o
Nice is this Jensen Huang response's to Alex Karp recent statement on AI companies stealing people data with a subvention (token price) ? Because building ontologies is Palantir's core business.
The readme seems to be written for enterprise project managers. Does anyone know what philosophy or academic ontology work this ties to, if any? I’m having trouble understanding the specifics, I’ll be honest…
how to use it
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Surprised that https://github.com/ucbepic/DataAgentBench hasn't been mentioned on this: benchmarking data agents that need to query from multiple sources is a focus of some research at UC Berkeley -- leaderboard is worth a look to see how the various closed and open-source implementations build ontologies ... would like to see how Nvidia's approach stacks up.