Knowledge Graphs & LLMs: Fine-Tuning Vs. Retrieval-Augmented Generation
What are the limitations of LLMs, and how to overcome them
The future of knowledge graphs in times of Large Language Model (LLMs)
🔥✌️The future of knowledge graphs in times of Large Language Model (LLMs).👇 How knowledge will be stored and structured, e.g. by Google, in the future is a… | 15 comments on LinkedIn
The future of knowledge graphs in times of Large Language Model (LLMs)
Comparing ChatGPT responses using statistical similarity v knowledge representations in the automated text selection process
I’ve been comparing ChatGPT responses using statistical similarity v knowledge representations in the automated text selection process. There are token…
comparing ChatGPT responses using statistical similarity v knowledge representations in the automated text selection process
Explore OntoGPT for Schema-based Knowledge Extraction
The OntoGPT framework and SPIRES tool provide a principled approach to extract knowledge from unstructured text for integration into Knowledge Graphs (KGs), using Large Language Models such as GPT. This methodology enables handling complex relationships, ensures logical consistency, and aligns with predefined ontologies for better KG integration.
The OntoGPT framework and SPIRES tool provide a principled approach to extract knowledge from unstructured text for integration into Knowledge Graphs (KGs), using Large Language Models such as GPT. This methodology enables handling complex relationships, ensures logical consistency, and aligns with predefined ontologies for better KG integration
Structure-inducing pre-training
Nature Machine Intelligence - Designing methods to induce explicit and deep structural constraints in latent space at the sample level is an open problem in natural language processing-derived...
Harnessing the Power of Knowledge Graphs for Language Model Governance
Are you considering deploying a language model like ChatGPT in your organisation, but have concerns about risk and governance? 🤔 🔗 Harnessing the Power of… | 32 comments on LinkedIn
AI’s Woolf at the door — LLMs and Knowledge Graphs
I was at the Knowledge Graph Conference this week, and as usual, it was a fun and insightful time. I have enough topics for at least four…
Explaining rules with LLMs
At the Knowledge Graph Conference last week, a number of data managers and researchers from IKEA presented their work on a recommendation…
Ecosystem Graphs: The Social Footprint of Foundation Models
Researchers develop a framework to capture the vast downstream impact and complex upstream dependencies that define the foundation model ecosystem.
The 4 Trends That Prevail on the Gartner Hype Cycle for AI, 2021
Operationalizing AI initiatives and data AI are among major trends for CIOs to watch in 2021.
Top 10 Use Cases: Knowledge Graphs
Graph Neural Networks for Multi-Relational Data
From GCNs to R-GCNs: encoding the structure of Knowledge Graphs with neural architectures (examples in NumPy code)
PageRank algorithm, fully explained
Today’s post is about Google algorithm, commonly defined as the PageRank algorithm.