GraphNews

OMG! 341 papers have been published on the topic of RAG (Retrieval Augmented Generation) since Jan 1, 2024: Naive RAG, Advanced RAG, GraphRAG … !  Please tell…

This notebook converts CSV data into a Neo4j Graph Database. All you do is describe your data. Have you wanted to see what your data looked like as a graph…

We had a very interesting session about QLever (SPARQL engine) by Hannah Bast at the Knowledge Graph Forum two weeks ago, and the demos in her slides were…

Graph RAG can perform much better than std RAG. Here’s when and how: When you want your LLM to understand the interconnection between your documents before…

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge and experience hallucinations during reasoning, which can...

LinkML is a language for modeling linked data

Following ICLR Graph Papers, I've created a repo for ICML graph papers, grouped by topic. We've got around 250 papers focusing on Graphs and GNNs in ICML'24.…

I spoke with Juan Sequeda about knowledge graphs and how he's leveraging them in the product at data.world - he also spoke about some of the new features tha...

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After 6 years of using GraphQL in production we aren't reaching for it as much as we once did. First up, security. GraphQL's self-documenting query API… | 161 comments on LinkedIn

Copyright 2024. Kurt Cagle / The Cagle Report A lot of the chatter lately in the machine learning community is beginning to shift towards discussions about semantics, and whether or not machines can actually "understand" them in any meaningful sense.

Excited to share that our recent work "Static Graph Approximations of Dynamic Contact Networks for Epidemic Forecasting" is published at Scientific Reports…

Learn how graph and vector search systems can work together to improve retrieval-augmented generation (RAG) systems.

Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge...

Although they are often overlooked or downplayed, #taxonomies are critical for #DataManagement and #KnowledgeRepresentation, providing a structured framework… | 11 comments on LinkedIn

Wow, what a great farm-to-fork notebook by Jerry Liu that goes from 1) the exciting text of the San Francisco 2023 Budget Proposal (gnarly PDF!) all the way…

Using large language models to discern commonsense relationships can improve performance on downstream tasks by as much as 60%.

This week, I thoroughly enjoyed attending the 21st Extended #SemanticWeb Conference! Here’s a summary of the contributions presented at the conference about…

How SP used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models

How we used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models

At Semantic Partners, we wanted to build our informed opinion over the strengths and weaknesses of graph RAG for RDF triple stores. We considered a simple use case: matching a job opening with Curriculum Vitae. We show how we used Ontotext GraphDB to build a simple graph RAG retriever using open, offline LLM models – the graph acting like a domain expert for improving search accuracy.

A short introduction what a GQL query looks like relative to Cypher

Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a graph. Question Answering over KGs (KGQA) is the task of...

Introducing Docs2KG: A New Era in Knowledge Graph Construction from Unstructured Data ... Did you know that 80% of enterprise data resides in unstructured… | 13 comments on LinkedIn

Knowledge Graph Forum, Switzerland. Contribute to zazuko/knowledge-graph-forum development by creating an account on GitHub.

This specification defines an approach to represent generic forms of composite values (lists and maps, in particular) as literals in RDF, and corresponding extensions of the SPARQL language. These extensions include an aggregation function to produce such composite values, functions to operate on such composite values in expressions, and a new operator to transform such composite values into their individual components.

RDF combines universal ways to name, structure and give meaning to data using only open standards. Naming is done with URIs; the structure is always the subject-predicate-object triple, and the meaning is provided by extending RDF with shared vocabularies. These three ways, individually and in combination, enable autonomy and cohesion. Let's see how.

In this presentation, Hedden describes the components of an enterprise knowledge graph and provides further insight into the semantic layer.

When building GraphRAG, you may want to explicitly define the graph yourself, or use the LLM automatically extract the graph. Both have tradeoffs: the former… | 17 comments on LinkedIn