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?

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…

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 ...

Subscribe • Previous Issues Boosting LLMs with External Knowledge: The Case for Knowledge Graphs When we wrote our post on Graph Intelligence in early 2022, our goal was to highlight techniques for deriving insights about relationships and connections from structured data using graph analytics and machine learning. We focused mainly on business intelligence and machine learning applications, showcasing how technology companies were applying graph neural networks (GNNs) in areas like recommendations and fraud detection.

Vectors need Graphs! Embedding vectors are a pivotal tool when using Generative AI. While vectors might initially seem an unlikely partner to graphs, their… | 61 comments on LinkedIn

Editor's Note: This post was written by Tomaz Bratanic from the Neo4j team. Extracting structured information from unstructured data like text has been around for some time and is nothing new. However, LLMs brought a significant shift to the field of information extraction. If before you needed a team of

Discover an innovative roadmap, combining LLMs, KGs, and a potent content creation tool to revolutionize SEO-optimized content generation.

Overcoming the "Reversal Curse" in LLMs with Ontologies: The "Reversal Curse" is a term coined in a recent paper to describe a particular failure of… | 108 comments on LinkedIn

🚀 Exciting News: Introducing "Reasoning on Graphs (RoG)" - Unlocking Next-Level Reasoning for Large Language Models! 📊🧠 We are thrilled to unveil our… | 42 comments on LinkedIn

It was an honor to present the initial results of the Chat with the Data benchmark last week at the The Alan Turing Institute Knowledge Graph meetup (link to… | 11 comments on LinkedIn

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 hit the arXiv streets and made quite a bang! The paper outlines a model called InstructGLM that adds further evidence that the future of graph representation lea

The Memory Game: Investigating the Accuracy of AI Models in Storing and Recalling Facts … 🧠 ... Comparing LLMs and Knowledge Graph on Factual Knowledge I’m… | 18 comments on LinkedIn

No, this is not the chat a father has with Lucius L. Malfoy when he asks to walk your daughter along a path to the prom.

See how to integrate a language language model with TigerGraph to empower question answering with your connected data.

We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Our LLMs4OL paradigm investigates the following hypothesis: \textit{Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?} To test this hypothesis, we conduct a comprehensive evaluation using the zero-shot prompting method. We evaluate nine different LLM model families for three main OL tasks: term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS.

Leveraging knowledge graphs for LLMs using LangChain

Prompting an LLM with an ontology to drive Knowledge Graph extraction from unstructured documents