Entity Resolution: Priority #1 for Building Real Knowledge Graphs | LinkedIn
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.
How can we represent change in complex systems using Ontologies
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
How can we represent change in complex systems using Ontologies
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
Knowledge Graphs as Powerful Evaluation Tools for LLM Document Intelligence
Knowledge Graphs as Powerful Evaluation Tools for LLM Document Intelligence 📃 Organizations across industries are grappling with an unprecedented deluge of… | 57 comments on LinkedIn
Knowledge Graphs as Powerful Evaluation Tools for LLM Document Intelligence
When Marketing Met Knowledge Graphs and LLMs: Ontotext's Way | 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
Graph Artificial Intelligence in Medicine | Annual Reviews
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.
Build and deploy knowledge graphs faster with RDF and openCypher | Amazon Web Services
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 […]
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's Semantic Search: Going to the Dogs? | LinkedIn
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.
Fact Finder -- Enhancing Domain Expertise of Large Language Models...
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...
Introducing SEOntology: A Universal Framework for SEO Data | LinkedIn
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.
Common Elements of Data Quality in the Age of AI - DQLabs
[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 […]
We-KNOW RAG 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 to 𝗥𝗔𝗚 leverages a 𝗴𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 method
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…
𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 to 𝗥𝗔𝗚 leverages a 𝗴𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 method
Triple your knowledge graph speed with RDF linked data and openCypher using Amazon Neptune Analytics | Amazon Web Services
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.
