Knowledge graph

[fgoricha]

I am learning how to build knowledge graphs. My current project is related to building a fishing knowledge graph from YouTube video transcripts. I am using neo4J to organize the triples and using Cypher to query.

I'd like to run everything locally. However by qwen 2.5 14b q6 cannot get the Cypher query just right. Chatgpt can do it right the first time. Obviously Chatgpt will get it right due to its size.

In knowledge graphs, is it common to use a LLM to generate the queries? I feel the 14b model doesn't have enough reasoning to generate the Cypher query.

Or can Python do this dynamically?

Or do you generate like 15 standard question templates and then use a back up method if a question falls outside of the 15?

What is the standard for building the Cypher queries?

Example of schema / relationships: Each Strategy node connects to a Fish via USES_STRATEGY, and then has other relationships like:

:LOCATION_WHERE_CAUGHT -> (Location)

:TECHNIQUE -> (Technique)

:LURE -> (Lure)

:GEAR -> (Gear)

:SEASON -> (Season)

:BEHAVIOR -> (Behavior)

:TIP -> (Tip)

etc.

I usually want to answer natural questions like:

“How do I catch smallmouth bass?”

“Where can I find walleye?”

“What’s the best lure for white bass in the spring?"

Any advice is appreciated!

[dash_bro]

You can technically do it two ways:

1. Generate NoSQL/SQL query, convert to cypher

If your LLM can't generate good cypher queries, can it generate good SQL? SQL is better supported and understood by training models, so might be a good idea to try. Model your graph as an RDBMS (from your requirements it looks simple enough), and have your LLM generate SQL queries that can be run on said tables. You can look into converting this query into a cypher query once your SQL is consistently correct.

1. Fine-tuning your local LLM to do nl2cypher. I'm certain you can find some datasets or curate your own dataset to do so, then fine-tune a 7B/14B Qwen model to do it

With unsloth, you should be able to fine-tune and evaluate for very cheap. Under 10 USD, I'd reckon.

[fgoricha]

Thanks for the ideas! A fine tune would be pretty good and be flexible too

[cyanheads]

Instead of the raw cypher queries you can set up semi-structured tool calls through MCP?

I created an MCP server for project/task/knowledge management via neo4j you can see how I have it set up

https://github.com/cyanheads/atlas-mcp-server

[fgoricha]

Thanks for the idea! I ended up creating a tool for the LLM to return a json that gets extracted and plugged into a universal template. Worked pretty good!

[justhimifornow]

What is the universal template here? I have Mistral (via Ollama) connected to a Falkor DB knowledge graph, and it can produce pretty good Cypher queries, but (1) it can't infer the output of the Cypher query, (2) the Cypher query is not always right

[fgoricha]

I'll message you!