GraphNews

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

Sign in or join now to see posts like this one and more.

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.

What process and features are available for you to understand and iterate on the Schema, Graph Information, and Question interpretation.

Latest findings in multiple research directions for handling graph prediction and optimisation

Understanding the challenges of Generative AI in the age of open web. How GenAI technology can shape public discourse and information quality.

Introduction In my first article on LinkedIn, I discussed how to build an ontology from a corpus of text. My follow-up research for agnos.

Recent advancements in Large Language Models (LLMs) have showcased their proficiency in answering natural language queries. However, their effectiveness is hindered by limited domain-specific...

We're excited to announce the first release of SEOntology, an open-source framework developed to unify and standardize SEO data across the industry. SEOntology aims to address one of the pressing challenges in modern SEO: data fragmentation.

[vc_row][vc_column width=”1/2″ css=”.vc_custom_1724344160150{padding-right: 70px !important;}”][vc_column_text]In the era of AI, data quality has become a critical factor for successful implementations. A study by MIT Sloan Management Review found that 85% of executives believe AI will offer significant competitive advantages, yet only 39% have an AI strategy in place. This gap highlights the importance of foundational elements […]

Scott Meyer discusses LIquid, the graph database built to host LinkedIn, serving a ~15Tb graph at ~2M QPS.

Passing this along (because I think it shows how this field is evolving) but also to make a point. RAG is only half the story. Use RDF2Vec or a similar encoder…

There are numerous publicly available Resource Description Framework (RDF) datasets that cover a wide range of fields, including geography, life sciences, cultural heritage, and government data. Many of these public datasets can be linked together by loading them into an RDF-compatible database. In this post, we demonstrate how to build knowledge graphs with RDF linked data and openCypher using Amazon Neptune Analytics.

Taxonomies and ontologies together are increasingly described as semantic models or semantic structures.