Rogue Scholar Posts

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Published in Stories by Research Graph on Medium
Author Xuzeng He

Latest findings for the use of Knowledge Graph in the field of QA in multiple research directions Author: Xuzeng He ( ORCID: 0009–0005–7317–7426) Question Answering (QA), the ability to interact with data using natural language questions and obtaining accurate results, has been a long-standing challenge in computer science dating back to the 1960s.

Published in Stories by Research Graph on Medium

Unlocking the power of knowledge graphs in research catalogues: A deep dive into OpenAlex Author: Dhruv Gupta (ORCID: 0009–0004–7109–5403 ) Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Published in Stories by Research Graph on Medium
Author Wenyi Pi

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and Knowledge Graphs How knowledge-enhanced language models and knowledge graphs are advancing open-domain conversational question answering Author: Wenyi Pi (ORCID: 0009-0002-2884-2771 ) When searching for information on the website, it is common to come across a flood of

Published in Stories by Research Graph on Medium
Author Amanda Kau

Unlocking the Power of Questions — A deep dive into Question Answering Systems Author: Amanda Kau (ORCID: 0009–0004–4949–9284 ) Virtual assistants have popped up on numerous websites over the years.

Published in Stories by Research Graph on Medium

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.

Published in Stories by Research Graph on Medium

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.

Published in Stories by Research Graph on Medium

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.

Published in Stories by Amir Aryani on Medium

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.