GraphNews

The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities. However, current approaches often...

✨ Operationalizing the information architecture 👇 There are three main ways to operationalize the information architecture, depending on how the data plane… | 14 comments on LinkedIn

A Step-by-Step Tutorial on Implementing Retrieval-Augmented Generation (RAG), Semantic Search, and Recommendations

"From Semiotic to Digital Enterprise Semantic Interoperability? Translation in english with some completion of my last article in French. Enterprises are…

GraphRAG uses LLM-generated knowledge graphs to substantially improve complex Q&A over retrieval-augmented generation (RAG). Discover automatic tuning of GraphRAG for new datasets, making it more accurate and relevant.

Have you ever entered a news-spelunking time-machine 🧗? Well AskNews built one...and our users are already jumping into the time-machine to explore the…

An example of the application of #LegalKit is the production of knowledge #graphs, here is a Hugging Face demo #Space 🤗 With the update of the French legal…

Where do you start when you want to build an ontology? Building an ontology sounds like a big, complex task, right? With all those high-level frameworks like… | 28 comments on LinkedIn

Establishing this open data portal to share our reference data and schema with customers, stakeholders, and partners under a permissive open data license.

I keep seeing mentions of "entity-resolved knowledge graphs", which leads me to believe that other so-called "knowledge" graphs don't resolve their entities. But a knowledge graph without entity resolution is like a beach under deep snow and dark skies.

Hype Cycle for Data Management, 2024

How can we represent change in complex systems using Ontologies? In complex systems, understanding and representing change over time can be challenging. Basic… | 52 comments on LinkedIn

A step-by-step guide using Python

This is "Knowledge Graphs for Dummies! - Veronika Heimsbakk" by JavaZone on Vimeo, the home for high quality videos and the people who love them.

This article was based on Transmute Solutions Architect Nis Jespersen’s ‘UN/CEFACT Linked Data’ presentation from December 2022.

Knowledge Graph technologies are promising to implement in Business Intelligence Systems due to encoding different relations between…

LDBC TUC: a focus on graph data in China Shanghai -- We’ve recently come out of two long, interesting days at LDBC’s 18th Technical Users Committee meeting in Guangzhou, in southern China. This post largely concentrates on one point that came up twice at the meeting: how to define subgraphs to be ex

How Knowledge Graphs aid in Complete Retrieval for Multi-Document RAG

Knowledge Graphs as Powerful Evaluation Tools for LLM Document Intelligence 📃 Organizations across industries are grappling with an unprecedented deluge of… | 57 comments on LinkedIn

A story about Ontotext's enterprise-grade knowledge graph for marketing content, SEO and knowledge management. In 2022 I had the chance to walk the thorny, as I would later find out, road of my PhD thesis talk towards a vision of marketing where we don’t manipulate the marketing mix, but rather mana

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In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

Amazon Neptune Analytics now supports openCypher queries over RDF graphs. When you build an application that uses a graph database such as Amazon Neptune, you’re typically faced with a technology choice at the start: There are two different types of graphs, Resource Description Framework (RDF) graphs and labeled property graphs (LPGs), and your choice of […]

Discover more about what's new at AWS with Amazon Neptune Analytics now supports openCypher queries over RDF Graphs

Copyright 2024 Kurt Cagle / The Cagle Report From one of my readers: Hi Kurt, have you written any article on how a knowledge graph differ from inventory built using graph. We are in constant struggle to differentiate knowledge graph from inventory apps? Questions like can inventory systems built us

**!!!! Great Talk with Bradley Rees NVIDIA RAPIDS cuGraph lead at KDD 24 Conference !!** We had an excellent discussion about the cuGraph user experience in…

Google is the undisputed leader in web search – technically a monopoly in fact. The coverage of web properties (good and bad) is vast – about 400 billion documents – so in quantitative terms it's really very good.