An architecture review of Neo4j as both vector and graph store

On August 14, 2023, the paper Natural Language is All a Graph Needs by Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu and Yongfeng Zhang…

This week Alexander Erdl and I did our last #GoingMeta of the year, and we talked about Advanced #RAG patterns with #KnowledgeGraphs and vector search. We…

Large language models (LLMs), such as ChatGPT and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and newly found emergent capability (e.g., reasoning). While LLMs are mainly designed to process pure texts, there are many real-world scenarios where text data are associated with rich structure information in the form of graphs (e.g., academic networks, and e-commerce networks) or scenarios where graph data are paired with rich textual information (e.g., molecules with descriptions). Besides, although LLMs have shown their pure text-based reasoning ability, it is underexplored whether such ability can be generalized to graph scenarios (i.e., graph-based reasoning). In this paper, we provide a systematic review of scenarios and techniques related to large language models on graphs. We first summarize potential scenarios of adopting LLMs on graphs into three categories, namely pure graphs, text-rich graphs, and text-paired graphs. We then discuss detailed techniques for utilizing LLMs on graphs, including LLM as Predictor, LLM as Encoder, and LLM as Aligner, and compare the advantages and disadvantages of different schools of models. Furthermore, we mention the real-world applications of such methods and summarize open-source codes and benchmark datasets. Finally, we conclude with potential future research directions in this fast-growing field. The related source can be found at https://github.com/PeterGriffinJin/Awesome-Language-Model-on-Graphs.

When reading lengthy or intricate texts, keeping an overview of different dependencies within the context is crucial. Traditionally, humans achieve this through note-taking or mentally creating a concept map. Now imagine having AI at hand which generates such a map for you. Even better, the…

Enhancing AI Accuracy: Telco Network Knowledge Graph's Role in Overcoming LLM Hallucinations LLMs are known to create responses that, while appearing valid…

This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness.

I really enjoyed the latest #UnconfuseMe with Bill Gates and Yejin Choi.  Yejin's research is on symbolic knowledge distillation, which means they take large…

The advent of large language models (LLMs) , trained on vast amounts of text data, has been one of the most significant breakthroughs in…

Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success in various domains, have also been leveraged in graph-related tasks to surpass traditional Graph Neural Networks (GNNs) based methods and yield state-of-the-art performance. In this survey, we first present a comprehensive review and analysis of existing methods that integrate LLMs with graphs. First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i.e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks. Then we systematically survey the representative methods along the three categories of the taxonomy. Finally, we discuss the remaining limitations of existing studies and highlight promising avenues for future research. The relevant papers are summarized and will be consistently updated at: https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks.

Note: This article and the underlying LLM application were developed with Alexander Gilmore, Associate Consulting Engineer at Neo4j.

We're excited to announce the partnership between Neo4j and AWS to speed up enterprise AI development and resolve key LLM challenges.

Before gearing up for yet another AI project, ask if it's really the best approach.

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

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

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

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

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…