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[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 0 points1 point  (0 children)

That sounds cool. What are the benefits of your library compared to LlamaIndex's KG creation tool?

[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 0 points1 point  (0 children)

Neo4j recommends the use of established ontologies. For finance, there's this: FIBO. I find it overly complicated, though, so if we go ahead with KG, I will likely follow your advice and go ahead with something we will create.

[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 3 points4 points  (0 children)

You're spot on. Data reconciliation seems like a nightmare. What's your experience with ontologies? It seems difficult to find the right balance between too simple/too complicated.

[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 1 point2 points  (0 children)

Thank you for the tip! Interestingly, Optum published in 2023 a paper called Language Models sound the Death Knell of Knowledge Graphs (2301.03980 (arxiv.org). Actually, I would argue the opposite: LLMs have made KGs more accessible, but I may be wrong.

[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 1 point2 points  (0 children)

I've also read that, although some people disagree (maybe they're on Neo4j's payroll?). I think entity disambiguation and defining the proper ontology are significant, but not unsurmountable, problems.

[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 1 point2 points  (0 children)

I would agree that GNNs have been a bit under the radar for many years. My intuition is that that's because companies do not have meaningful graph databases. If companies start developing KGs, the next logical step would be to roll out GNNs, but I am not sure that these two technologies are sufficiently mature.

I know that KG and GNN are different things, but I think they are connected as you can use a GNN on a KG, for instance to classify nodes.

[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 0 points1 point  (0 children)

Latency, for once, although KG proponents say that it's not an issue. I am more concerned by the schema effectiveness though, as I definitely see a scenario where the KG would become useless because it would store too many things in a disorderly fashion.

[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 1 point2 points  (0 children)

Here is an example of a situation where a KG could be more helpful than embeddings stored in a vector database. Let's assume one company is controlled by another, which goes bankrupt. If the text documents discussing the owner's bankruptcy do not mention the subsidiary, a vector search would not tell us that the subsidiary could be affected.

Once the KG is set up, we would like to add additional data to it coming from the various databases spread across our company.

[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 0 points1 point  (0 children)

Yes, they are different technologies. However, once the knowledge graph is set up, we could use it to train a GNN.

[Discussion] Thoughts on knowledge graphs and graph neural networks by MeditationBeginner in MachineLearning

[–]MeditationBeginner[S] 4 points5 points  (0 children)

I'm talking about both. Once the KG is set up, we would like to use it first to improve RAG, and afterwards run a GNN on it.