Trend is Your Friend: Knowledge Graphs at the Heart of Gartner’s Impact Radar — Here is How the Decentralized Knowledge Graph (DKG) Enhances Reliable AI
Gartner puts Knowledge Graphs at the epicenter of their 2024 Impact Radar right next to Generative Artificial Intelligence (GenAI). Here is WHY knowledge graphs (KGs) are so important for Artificial Intelligence (AI) and how Decentralized Knowledge Graph (DKG) helps power trust at Internet scale.
Knowledge Graphs in support of reliability of the GenAI
KGs act as intelligent maps for AI, aiding in understanding connections, explaining decisions, enhancing learning through Retrieval-Augmented Generation (RAG) to reduce hallucination and bias in AI.
Initially developed by Meta, RAG is a type of AI system that combines two main tasks: retrieving information and generating answers. Think of it like a smart assistant that not only looks up facts but also puts them together in a way that makes sense. By using KGs, RAG systems become smarter and more reliable.
They can understand questions better, provide accurate answers, connect different ideas, search quickly, show their sources, and bring together knowledge from various topics. This helps users find the information they need and helps drive more reliable AI based on the transparent use of knowledge sources.
Decentralized Knowledge Graph (DKG): Trust the Source
“We live in a time of abundant connectivity and alas abundant misinformation. The OriginTrail Decentralized Knowledge Graph (DKG) is an evolving tool for finding the truth in knowledge. In particular, we see knowledge graphs improving the fidelity of artificial intelligence.”
Dr. Bob Metcalfe, Internet pioneer and Ethernet creator
Driving data interconnectivity, interoperability, and integrity, the Decentralized Knowledge Graph (DKG), importantly advances knowledge graphs (KGs). It addresses the challenges of data ownership, AI hallucinations, and bias with the Decentralized Retrieval-Augmented Generation (dRAG) that enhances RAG by organizing external sources beyond a single organization in a DKG for AI models to use — from a single source to networks of sources.
Most importantly, the DKG allows users to go beyond the limitations of siloed data of a single organization to achieve integrated, decentralized knowledge access from multiple sources, all while preserving the lineage, or provenance of it all. This then drives the reliability and accuracy of AI well beyond a single source. The DKG also provides a connective layer (or a middleware) that allows for centrally operated knowledge graphs to interoperate with other information sources.
Are you building an enterprise-grade AI solution that requires reliability and trust beyond a single source?
Go check the solutions used by world-class organizations built on OriginTrial DKG and schedule a demo.
