GraphNews

At Microsoft, we strive to constantly learn and share our insights across innovation, culture and governance. Today, as we reflect on our progress since launching the Secure Future Initiative one year ago, I want to share a little more about our vision for a graph-powered platform future.

WhyHow’s Knowledge Graph Studio for modular agentic RAG systems.

My takeaways from the International Semantic Web Conference #iswc2024 Ioana keynote: Great example of data integration for journalism, highlighting the use of…

I'm coming around to the idea of ontologies. My experience with entity extraction with LLMs has been inconsistent at best. Even running the same request with… | 63 comments on LinkedIn

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy ⛳ In the realm of agentic systems, a fundamental challenge emerges…

📑 I’ve been out of the grid the past few weeks to wrap up my upcoming book. 🚀 After 12 months of work that turned out to be more intense than I expected… | 20 comments on LinkedIn

A Python hands-on guide to understand the principles of generating new knowledge by following logical processes

15 November 2024 Bob Kasenchak, Factor As taxonomy consultants at Factor, we are frequently engaged to help our clients clean up and improve existing taxonomies; these have often developed ad hoc and, often, have not been constructed or managed by taxonomists. This is fine! Having controlled lists o

👉 Our paper "Understanding SPARQL Queries: Are We Already There?", explores the potential of Large Language Models (#LLMs) to generate natural-language…

Retrieval-augmented generation (RAG) helps AI systems provide more information to a large language model (LLM) when generating responses to user queries. A new method for conducting “global” queries can optimize the performance of global search in GraphRAG.

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Showcase of Data Transformation Process, Breakdown of 25 dev hours involved, Schemas used, Questions & Responses, and Graph created

Introduction

I spent the day in San Francisco yesterday attending part 2 of Neo4j's GenAI Graph Gathering. Along with the original session back in May, this was one of the…

Updating an ontology or graph data model has a lot of implications. How much change does the update cause, is the change breaking any up or downstream applic...

As I did my PhD in network and data science, graphs are really close to me. Now, if you would like to get a tast of why these fields are so exciting and…

Causal discovery with panel data? Welcome tigramite Python package. (Note that I am still unsure about causal discovery applications.) I can't find who…

In Parallel’s Intelligent Operating Model uses AI to align strategy with execution, improving value creation and operational efficiency to meet today’s challenges.

AIs will only be safe for general use when they have and use goals and values that are identical to those of humans. In theory, the particular goals and values – very much like Asimov's original Laws of Robotics – could be legislated and enforced, so that we would all be safe from harm from AI.

It’s decision time for Galaxy users as Samsung responds to new iPhone threat.

The world of graph technology is vast and evolving. We're here to help you make sense of the key trends and players.

A deep dive into LangChain’s implementation of graph construction with LLMs

PostgreSQL to Graph querying in less than 5 minutes

While PuppyGraph is less than a year old, it is already witnessing success with several enterprises, including Coinbase, Clarivate, Dawn Capital and Prevelant AI.

Large Language Models (LLMs) are revolutionizing AI capabilities, but organizations face challenges in reducing inaccuracies and protecting valuable data. Knowledge Graphs offer a solution, helping improve LLM accuracy and safeguard organizational data assets. Learn how implementing Knowledge Graphs can address these critical issues and maintain competitiveness in the AI landscape.

A question-based extraction approach

From DAGs to DOGs: Data Object Graph (DOG), queryable, adaptable traversable hybrid data and execution graphs built for AI and traditional analytics use-cases… | 59 comments on LinkedIn

We are launching a series of benchmarks to prove the value of GraphRAG for question & answering on complex documents. The process is simple, we ingest the…

🌐 Unlock the Power of Causal Graphs: Go Beyond Explainability with PyWhy! Hey, #GraphGeeks! Don’t miss Amy Hodler and Michelle Yi’s session at Open Data…