Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 11 comments on LinkedIn

Making LLM Knowledge Graph building fast & dynamic: challenges, design decisions, and solutions

Image by David from Pixabay Mobile phones make it possible to secure and manage personal data on-device, which opens up a novel opportunity for both phone owners and device manufacturers: AI personalization via a data resource that stays on the phone. With the right design, personal knowledge graph on-device could provide contextualization while at the… Read More »Samsung and Apple’s knowledge-centric approaches to secure, personalized AI on phones

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

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...

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.

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

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

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…

Introduction: In Q1 2025, SAP is planning to release the knowledge graph engine as a new capability for SAP HANA Cloud platform. This new multi-model engine allows businesses to connect and analyze information more effectively. SAP HANA Cloud will enable smarter applications and improve user experie...

Following its successful Series A funding, DevRev drives forward its mission to revolutionize customer support and product development.

Can Ontologies be seen as General Ledger for AI? Could that be a good way to audit AI systems delivering critical business outcomes? In my quest to develop a… | 69 comments on LinkedIn

Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the...

The Cross-Domain Interoperability Framework (CDIF) is designed to support FAIR implementation by establishing a ‘lingua franca’, based on existing standards and technologies to support interoperability, in both human- and machine-actionable fashion. CDIF is a set of implementation recommendations, based on profiles of common, domain-neutral metadata standards which are aligned to work together to support core functions required by FAIR. This report presents a core set of five CDIF profiles, which address the most important functions for cross-domain FAIR implementation.  Discovery (discovery of data and metadata resources) Data access (specifically, machine-actionable descriptions of access conditions and permitted use) Controlled vocabularies (good practices for the publication of controlled vocabularies and semantic artefacts) Data integration (description of the structural and semantic aspects of data to make it integration-ready) Universals (the description of ‘universal’ elements, time, geography, and units of measurement). Each of these profiles is supported by specific recommendations, including the set of metadata fields in specific standards to use, and the method of implementation to be employed for machine-level interoperability. A further set of topics is examined, establishing the priorities for further work. These include: Provenance (the description of provenance and processing) Context (the description of ‘context’ in the form of dependencies between fields within the data and a description of the research setting) Perspectives on AI (discussing the impacts of AI and the role that metadata can play) Packaging (the creation of archival and dissemination packages)  Additional Data Formats (support for some of the data formats not fully supported in the initial release, such as NetCDF, Parquet, and HDF5).  In each of these topics, current discussions are documented, and considerations for further work are provided. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393.