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People probably don't discuss the problems enough about an open world knowledge graph. Essentially the same class of problems as spam filters. Using an open lan

by th0ma5 1y ago

People probably don't discuss the problems enough about an open world knowledge graph. Essentially the same class of problems as spam filters. Using an open language model to produce a graph doesn't create a closed world graph by definition. This confusion as well as just general avoidance of measuring actual productivity outcomes seems like an insurmountable problem in knowledge world now and I feel language itself is failing at times to educate on this issues.

lyu07282 1y ago
They don't even do any entity disambiguation, the resulting graph won't be very useful indeed. I also saw people then use a different prompt to generate a cypher query from user input for RAG, I can't imagine that actually works well. It would make a little more sense if they then use knowledge graph embeddings, but I'm not sure if neo4j supports that.
- badmonster 1y ago
  Entity resolution is a great topic and there’s lots of research in this area. This is something I’m looking into next. I’m thinking about - Metadata based match (I’ve done that with search system in the past) - Embedding base match (false positive is definite consideration - Using knowledge graph itself to do entity resolution before feeding the entities to graph next Add human in the loop to guide entity resolutions. What do you think? Would love to learn your thoughts:)
- badmonster 1y ago
  btw, Neo4j supports vector properties and building vector index out of it. It's supported in cocoindex to configure a vector index: https://cocoindex.io/docs/ops/storages#neo4j https://cocoindex.io/docs/ops/storages#neo4j Neo4j also supports building embedding leveraging more information in the graph in addition to single node's property: https://neo4j.com/docs/graph-data-science/current/machine-learning/node-embeddings/ https://neo4j.com/docs/graph-data-science/current/machine-le... (It's hard to incrementally compute them, but users can still compute them after the graph is built) looking forward to learn your thoughts :)

by th0ma5 1y ago

lyu07282 1y ago

They don't even do any entity disambiguation, the resulting graph won't be very useful indeed. I also saw people then use a different prompt to generate a cypher query from user input for RAG, I can't imagine that actually works well. It would make a little more sense if they then use knowledge graph embeddings, but I'm not sure if neo4j supports that.

badmonster 1y ago
Entity resolution is a great topic and there’s lots of research in this area. This is something I’m looking into next. I’m thinking about - Metadata based match (I’ve done that with search system in the past) - Embedding base match (false positive is definite consideration - Using knowledge graph itself to do entity resolution before feeding the entities to graph next Add human in the loop to guide entity resolutions. What do you think? Would love to learn your thoughts:)
badmonster 1y ago
btw, Neo4j supports vector properties and building vector index out of it. It's supported in cocoindex to configure a vector index: https://cocoindex.io/docs/ops/storages#neo4j https://cocoindex.io/docs/ops/storages#neo4j Neo4j also supports building embedding leveraging more information in the graph in addition to single node's property: https://neo4j.com/docs/graph-data-science/current/machine-learning/node-embeddings/ https://neo4j.com/docs/graph-data-science/current/machine-le... (It's hard to incrementally compute them, but users can still compute them after the graph is built) looking forward to learn your thoughts :)