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Case Studies, how Public companies are using KGs today, and where to apply KGs in RAG

OfficeGraph is a knowledge graph of IoT device measurements. - RoderickvanderWeerdt/OfficeGraph

Last week, I attended the 21st Extended (European) Semantic Web Conference. The conference was well organised by Dr. Albert Meroño Peñuela from King’s College London. He seemed surprisingly c…

We are excited to announce WhyHow.AI’s Open Source Knowledge Graph schema library.

"𝗚𝗿𝗮𝗽𝗵 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗼𝗻 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀" 📑 -- a Paper from a long-term project in my PhD has finally been released!…

Use of a 20 frames per second (fps) RGB camera plus an event camera can achieve the same latency as a 5,000-fps camera with the bandwidth of a 45-fps camera without compromising accuracy.

Build-your-own Graph RAG 🕸️ There are two prepackaged ways to do RAG with knowledge graphs: vector/keyword search with graph traversal, and text-to-cypher.… | 15 comments on LinkedIn

GNN-RAG Combines the language understanding abilities of LLMs with the reasoning abilities of GNNs in a RAG style. The GNN extracts useful and relevant…

Discover how adding structured data to your podcast site can boost your SEO and enhance search engine visibility with our guide.

SCOOP: Shape Integration Framework. Contribute to dtai-kg/SCOOP development by creating an account on GitHub.

🎉 Check out our new work on Transformer theory! (out today on arxiv) Key takeaways: 1️⃣ We show how 9 different algorithmic tasks map into a complexity… | 10 comments on LinkedIn

💡 Knowledge Graphs for mimicking human memory to integrate “new experiences” in LLMs. 🔬 In a paper entitled “HippoRAG: Neurobiologically Inspired Long-Term…

I presented this (with some variation) a few days ago, following a post I wrote a few weeks ago about named node expressions and reifications in RDF, Turtle…

Subscribe • Previous Issues Enhancing RAG with Knowledge Graphs: Blueprints, Hurdles, and Guidelines By Ben Lorica and Prashanth Rao. GraphRAG (Graph-based Retrieval Augmented Generation) enhances the traditional Retrieval Augmented Generation (RAG) method by integrating knowledge graphs (

This is a continuation of my previous article on creating a Knowledge Graph in 100 lines of code. In this article I will show you how you can use the “chat with your data” paradigm to c…

AutoMR with Graph-Based Models for O-RAN A pivotal innovation propelling the telecom transformation can be the integration of Automated Machine Reasoning…

Background: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease’s etiology and response to drugs. Objective: We designed the Alzheimer’s Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. Methods: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. Results: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. Conclusions: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

PDF | There are thousands of cultural events occurring across UK weekly, such as theatre, comedy shows, festivals, exhibitions etcetera, leaving huge... | Find, read and cite all the research you need on ResearchGate

Cite as: Hong Yung Yip and Amit Sheth, "..

🔍 How to Start with Graph Neural Networks for Time Series Forecasting❓ 📈 As Large Language Models continue to evolve, there are many debates about whether… | 21 comments on LinkedIn

💡 How important are learning paths for gaining the skills needed to tackle real-life problems? 🔬Researchers from the University of Siegen (Germany) and Keio…

The NORIA-O project is a data model for IT networks, events and operations information. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for rea...

We’re excited to launch a huge feature making LlamaIndex the framework for building knowledge graphs with LLMs: The Property Graph Index 💫 (There’s a lot of… | 57 comments on LinkedIn

Catch Thomas Smoker of WhyHow.AI talking with Demetrios Brinkmann of MLOps Community about "Managing Small Knowledge Graphs for Multi-agent Systems" Key…

In applications such as personal assistants, large language models (LLMs) must consider the user's personal information and preferences. However, LLMs lack the inherent ability to learn from user...

To Graph or Not to Graph Knowledge Graph Architectures and LLMs - Download as a PDF or view online for free

Revolutionizing Document Reranking with G-RAG: A Graph-Based Approach ... Discover how a novel graph-based reranker is transforming the way we retrieve…

Nine months ago, when my engineers told me that we should not fine tune an LLM for Legal and instead deepen our legal knowledge graph approach, it seemed like… | 34 comments on LinkedIn