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At Neo4j, we developed a new graph format that is more IO and CPU efficient and handles low memory situations better.

Large language models (LLMs) fit parameters (features in data topography) to a particular dataset, such as text scraped off the web and conformed to a training set.  Logical data models (LDMs), by contrast, model what becomes shared within entire systems. They bring together the data in a system with the help of various kinds of… Read More »How the LDMs in knowledge graphs can complement LLMs

MESH: A Flexible Distributed Hypergraph Processing System

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We deal with these things Transformer and Geometric graph improvement room

This post talks about the nature and key characteristics of knowledge graphs. It also outlines the benefits of formal semantics and how…

Go beyond typical RAG strategies

Augmenting Large Language Models with Hybrid Knowledge Architectures 🧐 Vector search or Knowledge graph ? Why not both at the same time… Retrieval…

Rdflib-neo4j introduces a client-side evolution of neosemantics, empowering data ingestion in all cloud and local Neo4j deployments

By request, here are the slides from our #neurips2023 presentation yesterday! We really enjoyed the opportunity to present the different aspects of the work… | 18 comments on LinkedIn

Here's the video for my talk @ K1st World Symposium 2023 about the intersections of KGs and LLMs: https://lnkd.in/gugB8Yjj and also the slides, plus related…

"Okay guys, if you want to read one article about ontologies, taxonomy, and data, this is the one. Congratulations Graham.

While LLMs have shown remarkable prowess in understanding and generating text, they have a critical limitation.

Despite the fact that it affects our lives on a daily basis, most of us are unfamiliar with the concept of a knowledge graph. When we ask Alexa about tomorrow's weather or use Google to look up the latest news on climate change, knowledge graphs serve as the foundation of today's cutting-edge information systems. In addition, knowledge graphs have the potential to elucidate, assess, and substantiate information produced by Deep Learning models, such as Chat-GPT and other large language models. Knowledge graphs have a wide range of applications, including improving search results, answering questions, providing recommendations, and developing explainable AI systems. In essence, the purpose of this course is to provide a comprehensive overview of knowledge graphs, their underlying technologies, and their significance in today's digital world.

The evidence for the massive impact of KGs in NLQ keeps piling up - Here's one more paper that shows that knowledge graph based RAG (retrieval-augmentation)…

A new message-passing paradigm where every node can choose to either ‘listen’, ‘broadcast’, ‘listen & broadcast’ or ‘isolate’.

We’re excited to announce a new AI-powered Skills in Viva service that will help organizations understand workforce skills and gaps, and deliver personalized skills-based experiences.

One of our main focuses at Zazuko GmbH is to support government organizations in publishing multidimensional data in RDF format. To this end, we utilize the cube.

This is the first of a series of blogs wherein I describe what we believe is a superior pattern for creating highly performant, real-world…

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…

Knowledge graphs (KGs) comprise entities interconnected by relations of different semantic meanings. KGs are being used in a wide range of applications. However, they inherently suffer from incompleteness, i.e. entities or facts about entities are missing. Consequently, a larger body of works focuses on the completion of missing information in KGs, which is commonly referred to as link prediction (LP). This task has traditionally and extensively been studied in the transductive setting, where all entities and relations in the testing set are observed during training. Recently, several works have tackled the LP task under more challenging settings, where entities and relations in the test set may be unobserved during training, or appear in only a few facts. These works are known as inductive, few-shot, and zero-shot link prediction. In this work, we conduct a systematic review of existing works in this area. A thorough analysis leads us to point out the undesirable existence of diverging terminologies and task definitions for the aforementioned settings, which further limits the possibility of comparison between recent works. We consequently aim at dissecting each setting thoroughly, attempting to reveal its intrinsic characteristics. A unifying nomenclature is ultimately proposed to refer to each of them in a simple and consistent manner.

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…

The internals of a SHACL engine — how Ontotext GraphDB validates your data

Natural language interfaces to databases have long been a holy grail of both industry and academia. Recently, advances in large language…