Author Amir Aryani (ORCID: 0000-0002-4259-9774) Introduction In this article we look at Research Graph as an information model , and an approach to connect and capture the connections between research outputs, researchers and research activities. We explore the metadata model, and we discuss how to capture this graph in a Neo4j Graph Database.
An Introduction to Retrieval Augmented Generation (RAG) and Knowledge Graph Author Qingqin Fang (ORCID: 0009–0003–5348–4264) Introduction Large Language Models (LLMs) have transformed the landscape of natural language processing, demonstrating exceptional proficiency in generating text that closely resembles human language.
Authors Nakul Nambiar (ORCID: 0009-0009-9720-9233) Amir Aryani (ORCID: 0000-0002-4259-9774) Knowledge graphs, which offer a structured representation of data and its relationships, are revolutionising how we organise and access information.
Authors: Hui Yin, Amir Aryani As we discussed in our previous article “A Brief Introduction to Retrieval Augmented Generation (RAG)”, RAG is an artificial intelligence framework that incorporates the latest reliable external knowledge and aims to improve the quality of responses generated by pre-trained language models (PLM). Initially, it was designed to improve the performance of knowledge-intensive NLP tasks (Lewis et al., 2020). As
Tools and Platform for Integration of Knowledge Graph with RAG pipelines. Authors Aland Astudillo (ORCID: 0009-0008-8672-3168) Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Many users of chatbots such as ChatGPT, have encountered the problem of receiving inappropriate or incompatible responses. There are several reasons why this might happen.
A novel approach to improving the efficiency of text search in graph databases utilizing Neo4j, OpenAI, and Typesense. Authors Nakul Nambiar (ORCID: 0009–0009–9720–9233) Aishwarya Nambissan (ORCID: 0009–0003–3823–6609) The ability to use cutting-edge tools and frameworks is essential for staying ahead in the ever-changing field of technology.
Authors: Hui Yin , Amir Aryani With the increasing application of large language models in various scenarios, people realize that these models are not omnipotent. When generating dialogues (Shuster et al., 2021), the models often produce hallucinations, leading to inaccurate answers.
Dear subscriber, welcome to the second edition of the newsletter and also a warm welcome to all new subscribers. It has been an interesting week. In this edition I will talk about some work updates, a one-day workshop I’ve attended last week and I have a video recommendation.
Some quick notes on interface ideas for digital libraries and/or knowledge graphs. Recently there’s been something of an explosion in bibliographic tools to explore the literature.
On Tuesday I was in Canberra to visit the Australian National Insect Collection at CSIRO and give a talk on knowledge graphs.