Knowledge Graphs
OriginTrail, OxiGraph, Neo4j
Last updated
OriginTrail, OxiGraph, Neo4j
Last updated
Knowledge graphs are structured representations of knowledge, consisting of entities (nodes) and their relationships (edges). They provide a powerful framework for organizing and connecting disparate data, making it machine-readable and facilitating sophisticated reasoning. In academic research, especially in fields like biology and medicine, where data is vast and complex, knowledge graphs are becoming increasingly vital for AI applications.
You can start by having the Agent interact with a pre-built knowledge graph, either locally with OxiGraph or on Base through OriginTrail
Prerequisites
Docker installed
pnpm installed
Node.js installed
Setup Steps
Follow the setup instructions in
Start the Oxigraph server: (Or use OriginTrail's DKG)
Now your local oxigraph instance is ready to load the processed scientific papers from the sampleJsonLds
folder. More info .
Load JSON-LD data into Oxigraph/OriginTrail's DKG:
Now your oxigraph has the triples loaded!
(Optional) Start PostgreSQL with vector support:
Alternatively, you can use pglite instead of PostgreSQL (Eliza will give you that option when starting for the 1st time)
Start the development server:
Enable the hypothesis generation service:
Uncomment line 25 and 27 in
The type of knowledge graph, the structure, the algorithm to create the nodes and edges, all factor into how well a BioAgent can traverse the graph to make a hypothesis or reason about a scientific claim.
https://docs.origintrail.io/build-with-dkg/dkg-sdk/dkg-v8-js-client
329d4fd (Update knowledge graphs documentation)
https://docs.origintrail.io/build-with-dkg/dkg-sdk/dkg-v8-js-client
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