The 3-Step Process for creating RAG-Native Knowledge Graphs
What process and features are available for you to understand and iterate on the Schema, Graph Information, and Question interpretation.
Recent Advances in using Machine Learning with Graphs
Latest findings in multiple research directions for handling graph prediction and optimisation
Introducing SEOntology: The Future Of SEO In The Age Of AI
Understanding the challenges of Generative AI in the age of open web. How GenAI technology can shape public discourse and information quality.
Loading a Knowledge Graph with Ontology Validation | LinkedIn
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.
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 […]
LIquid: a Large-Scale Relational Graph Database
Scott Meyer discusses LIquid, the graph database built to host LinkedIn, serving a ~15Tb graph at ~2M QPS.
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.
Taxonomies and Ontologies as Semantic Models
Taxonomies and ontologies together are increasingly described as semantic models or semantic structures.
Editing schemas, ontologies, and SKOS taxonomies with VocBench
A free GUI tool.
Ontotext mentioned in more than 10 Gartner Hype Cycle reports
😎 Pretty proud to see Ontotext mentioned in more than 10 Gartner Hype Cycle reports over the last couple months! This demonstrates the success of our…
Ontotext mentioned in more than 10 Gartner Hype Cycle reports
Outperforming Claude 3.5 Sonnet with Phi-3-mini-4k for graph entity relationship extraction tasks
When you need fast and high-throughput graph extraction with better quality than Claude 3.5 Sonnet.
Microsoft GraphRAG with an RDF Knowledge Graph — Part 3
Using SPARQL and the Knowledge Graph for RAG
Graph Retrieval-Augmented Generation: A Survey
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an...
Graph reasoning with LLMs
Here are the slides from our tutorial today at #kdd2024! Notebooks are available at the tutorial website: https://lnkd.in/ejTrYtfe | 16 comments on LinkedIn
What It Takes To Build a Great Graph
Our world is composed of relationships. Who we know, how we interact, how we transact — graphs structure information in this way…
Announcing Spanner Graph | Google Cloud Blog
With Spanner Graph, you can analyze interconnected data using Google Cloud’s always-on, globally consistent, and virtually unlimited-scale database.
Knowledge Graph Developer Experience
Now that W3C RDF Star will put Semantic Graphs on-par with Labelled Property Graphs, it is time to address the final barrier, and that is the developer…
Overcoming 6 Graph RAG Hurdles
Recent advancements in LLMs have sparked excitement about their potential to organize and utilize Knowledge Graphs. Microsoft's GraphRAG is a good starting… | 10 comments on LinkedIn
How MINPROMPT Uses Graph-Based Techniques to Optimize Data Augmentation
How MINPROMPT Uses Graph-Based Techniques to Optimize Data Augmentation ⁉ https://lnkd.in/e64JJyid The quality and quantity of training data remain…
How MINPROMPT Uses Graph-Based Techniques to Optimize Data Augmentation
MINPROMPT
Try out Artifacts created by Claude users
Taxonomies, Ontologies, and Semantics in Tech Comm’s World of AI
Technical communicators: understand these AI skills to form your new portfolio: terminology management, taxonomy, ontology, semantic layer, knowledge graph and knowledge management in general
Must read papers on GNN
This repo covers the basics and latest advancements in Graph Neural Networks. 15k+ GitHub ⭐. https://lnkd.in/e6_7uYt9
Microsoft's GraphRAG is costly to implement due to high computational expenses
Microsoft's GraphRAG architecture surpasses traditional #RAG systems by integrating knowledge graphs with vector stores. By structuring information… | 24 comments on LinkedIn
Microsoft's GraphRAG is costly to implement due to high computational expenses
The Necessary Multi-Step Retrieval Process in Graph RAG Systems
The Necessary Multi-Step Retrieval Process in Graph RAG Systems 〽 Graph-based Retrieval-Augmented Generation (RAG) systems is a cutting-edge approach to… | 50 comments on LinkedIn
The Necessary Multi-Step Retrieval Process in Graph RAG Systems
HybridRAG: Integrating Knowledge Graphs and Vector Retrieval...
Extraction and interpretation of intricate information from unstructured text data arising in financial applications, such as earnings call transcripts, present substantial challenges to large...
Data Product Vocabulary (DPROD)
The Data Product (DPROD) specification is a
profile of the Data Catalog (DCAT) Vocabulary,
designed to describe Data Products.
This document defines the schema and provides examples for its use.
The Data Product (DPROD) specification is a
profile of the Data Catalog (DCAT) Vocabulary,
designed to describe Data Products.
This document defines the schema and provides examples for its use.
DPROD extends DCAT to enable publishers to describe Data Products and data services in a decentralized way.
By using a standard model and vocabulary, DPROD facilitates the consumption and aggregation of metadata
from multiple Data Marketplaces.
This approach increases the discoverability of products and services, supports decentralized data publishing,
and enables federated search across multiple sites using a uniform query mechanism and structure.
The namespace for DPROD terms is https://ekgf.github.io/dprod/#
The suggested prefix for the DPROD namespace is dprod
DPROD follows two basic principles:
Decentralize Data Ownership: To make data integration more efficient, tasks should be shared among multiple
teams. DCAT helps by offering a standard way to publish datasets in a decentralized manner.
Harmonize Data Schemas: Using shared schemas helps unify different data formats.
For instance, the DPROD specification provides a
common set of rules for defining a Data Product.
You can extend this schema as needed.
The DPROD specification builds on DCAT by connecting
DCAT Data Services to
DPROD Data Products using Input and
output ports.
These ports are used to publish and consume data from a Data Product.
DPROD treats ports as dcat data services,
so the data exchanged can be described using DCAT's highly expressive metadata around
distributions and
datasets.
This approach also allows you to create your own descriptions for the data you are sharing.
You can use a special property called
conformsTo from DCAT to link to
your own set of rules or guidelines for your data.
The DPROD specification has four main aims:
To provide unambiguous and sharable semantics to answer the question: 'What is a data
product?'
Be simple for anyone to use, but expressive enough to power large data marketplaces
Allow organisations to reuse their existing data catalogues and dataset infrastructure
To share common semantics across different Data Products and promote harmonisation
Using knowledge graphs to build GraphRAG applications with Amazon Bedrock and Amazon Neptune | Amazon Web Services
Retrieval Augmented Generation (RAG) is an innovative approach that combines the power of large language models with external knowledge sources, enabling more accurate and informative generation of content. Using knowledge graphs as sources for RAG (GraphRAG) yields numerous advantages. These knowledge bases encapsulate a vast wealth of curated and interconnected information, enabling the generation of responses that are grounded in factual knowledge. In this post, we show you how to build GraphRAG applications using Amazon Bedrock and Amazon Neptune with LlamaIndex framework.