GraphNews

Graph-based data is ubiquitous in enterprise, across the industry verticals, and increasingly needed for machine learning use cases…

Our state-of-the-art model delivers 10-day weather predictions at unprecedented accuracy in under one minute

Reference Architectures for Graph Databases on AWS - aws-samples/aws-dbs-refarch-graph

A method to convert any text corpus into a Knowledge Graph using Mistral 7B.

Living the dreamt of (Knowledge Graph with Connected Content) Some people find it awkward to talk love and greater meaning when it comes to code or… | 19 comments on LinkedIn

Investing in Knowledge Graph provides higher accuracy for LLM-powered question-answering systems. And ultimately, to succeed in this AI world, enterprises must… | 23 comments on LinkedIn

Some people say we can use "knowledge graph" almost any way we want. This view is incorrect, counterproductive, and dangerous.

Cracking the code of complex systems. It's not the 'Matrix'. 🕶️ A primer on Networks and Graphs. 🕸️ Networks represent the connections between discrete…

Chaotic mess or meaningful maps? 🌪️🌐 Zooming out reveals the hidden logic beneath. Let's unpack the key structures emerging in high-dimensional…

How Sparql Anything may be a game changer for knowledge graphs

What you can do with gpt-4 is pretty insane. You can ask it to create an RDF description from the first chapter of a story: https://lnkd.in/emuxaX6d (you can… | 19 comments on LinkedIn

We are at a stage now where there are increasingly mechanisms for taking information from LLMs in order to extract knowledge graphs as sets of triples…

Latest Gartner research on semantics suggests the following: 1) Data silos become entrenched and limit an organization’s capacity to draw insights from its…

Introducing MechGPT 🦾🤖 This project by Markus J. Buehler is one of the coolest use cases of 1) fine-tuning an LLM, and 2) generating a knowledge graph that… | 33 comments on LinkedIn

So, what are basically Knowledge Graphs and why do we need to use them?

A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests

Learn how to get GPT to ask complex questions in any language

Natural language processing made easy

Where does current work on graph technologies intersect with large language models, for building AI apps?

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 lead to incorrect reasoning processes and diminish their performance and trustworthiness. Knowledge graphs (KGs), which capture vast amounts of facts in a structured format, offer a reliable source of knowledge for reasoning. Nevertheless, existing KG-based LLM reasoning methods only treat KGs as factual knowledge bases and overlook the importance of their structural information for reasoning. In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning. Specifically, we present a planning-retrieval-reasoning framework, where RoG first generates relation paths grounded by KGs as faithful plans. These plans are then used to retrieve valid reasoning paths from the KGs for LLMs to conduct faithful reasoning. Furthermore, RoG not only distills knowledge from KGs to improve the reasoning ability of LLMs through training but also allows seamless integration with any arbitrary LLMs during inference. Extensive experiments on two benchmark KGQA datasets demonstrate that RoG achieves state-of-the-art performance on KG reasoning tasks and generates faithful and interpretable reasoning results.

Retrieve information that spans across multiple documents

Understanding data points through the context of their relationships

Our focus in this post is to extract information from unstructured text using LLMs

Building enterprise knowledge graphs as property graphs of billions of nodes / edges holding organizational knowledge to power your…

I've been struggling for a while with the definition of "ontology". One thing that finally occurred to me is that our notion of ontologies for the most part… | 53 comments on LinkedIn

Seamlessy implement information extraction pipeline with LangChain and Neo4j

In the era of information overload, sifting through vast amounts of data to provide accurate search results in an engaging and…

Working on a LangChain template that adds a custom graph conversational memory to the Neo4j Cypher chain, which uses LLMs to generate Cypher statements. This…

Have you ever dreamed of producing a Knowledge Graph (KG) from a pile of textual documents at the click of a button?

The emergence of Large Language Models (LLMs) has dramatically changed the natural language processing (NLP) landscape. The reason lies mainly in their ...