GraphNews

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 ...

Empower your organisation with responsible decision-making, leveraging the power of knowledge-enriched Generative AI

Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance. Reasoning on graphs is essential for drawing inferences about the relationships between entities in a complex system, and to identify hidden patterns and trends. Despite the remarkable progress in automated reasoning with natural text, reasoning on graphs with large language models (LLMs) remains an understudied problem. In this work, we perform the first comprehensive study of encoding graph-structured data as text for consumption by LLMs. We show that LLM performance on graph reasoning tasks varies on three fundamental levels: (1) the graph encoding method, (2) the nature of the graph task itself, and (3) interestingly, the very structure of the graph considered. These novel results provide valuable insight on strategies for encoding graphs as text. Using these insights we illustrate how the correct choice of encoders can boost performance on graph reasoning tasks inside LLMs by 4.8% to 61.8%, depending on the task.

Graph Theory and Its Implications: A Quantitative Point of View In the technical universe of quantitative finance, graph theory has been gaining prominence… | 15 comments on LinkedIn

One model to rule them all

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.

Knowledge Graph Alliance (KGA) Launches in Brussels The Knowledge Graph Alliance (KGA) is proud to announce its inauguration on Friday, 10th November, in… | 23 comments on LinkedIn

BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

Graph-based deep learning methods have become popular tools to process collections of correlated time series. Differently from traditional multivariate forecasting methods, neural graph-based predictors take advantage of pairwise relationships by conditioning forecasts on a (possibly dynamic) graph spanning the time series collection. The conditioning can take the form of an architectural inductive bias on the neural forecasting architecture, resulting in a family of deep learning models called spatiotemporal graph neural networks. Such relational inductive biases enable the training of global forecasting models on large time-series collections, while at the same time localizing predictions w.r.t. each element in the set (i.e., graph nodes) by accounting for local correlations among them (i.e., graph edges). Indeed, recent theoretical and practical advances in graph neural networks and deep learning for time series forecasting make the adoption of such processing frameworks appealing and timely. However, most of the studies in the literature focus on proposing variations of existing neural architectures by taking advantage of modern deep learning practices, while foundational and methodological aspects have not been subject to systematic investigation. To fill the gap, this paper aims to introduce a comprehensive methodological framework that formalizes the forecasting problem and provides design principles for graph-based predictive models and methods to assess their performance. At the same time, together with an overview of the field, we provide design guidelines, recommendations, and best practices, as well as an in-depth discussion of open challenges and future research directions.

Many global corporations are managing multiple supply chains, and they depend on those operations to not only deliver goods on time but to respond to divergent customer and supplier needs. According to a McKinsey study, it’s estimated that significant disruptions to production now occur every 3.7 years on average, adding new urgency to supply chain […]

This is the second chapter in our blog series on GPU-accelerated graph analysis using RAPIDS cuGraph. In the first chapter, we introduced…

"TacticAI: an AI assistant for football tactics" by Zhe W., Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Yoram Bachrach, Romuald Elie, Kevin… | 28 comments on LinkedIn

We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information ("LLM thoughts") are vertices, and edges correspond to dependencies between these vertices. This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing thoughts using feedback loops. We illustrate that GoT offers advantages over state of the art on different tasks, for example increasing the quality of sorting by 62% over ToT, while simultaneously reducing costs by 31%. We ensure that GoT is extensible with new thought transformations and thus can be used to spearhead new prompting schemes. This work brings the LLM reasoning closer to human thinking or brain mechanisms such as recurrence, both of which form complex networks.

Graph Databases should be the better choice for Retrieval Augmented Generation (RAG)! We have seen the debate RAG vs fine-tuning, but what about Vector… | 37 comments on LinkedIn

🔥 Let #LLM understand graphs directly? GraphGPT made it! 📢 GraphGPT is a Graph Large Language Model, which aligns Large Language Models (LLMs) with Graphs…

In today’s data-driven world, businesses are constantly seeking new ways to harness the immense potential of their data. The integration of…

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

Explore the rise of graph databases in NoSQL, aligning with human thinking, and discover how companies like Paypal and Adobe leverage Aerospike for real-time graph solutions.

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

How to develop a conceptual data model, such as an EER diagram, UML Class Diagram, or ORM model? Besides dropping icons here and there on an empty canvas, a few strategies exist for approaching it …