English

Disrupt Your Research Using Generative AI Powered ScienceSage

Information Retrieval 2025-02-27 v1

Abstract

Large Language Models (LLM) are disrupting science and research in different subjects and industries. Here we report a minimum-viable-product (MVP) web application called ScienceSage\textbf{ScienceSage}. It leverages generative artificial intelligence (GenAI) to help researchers disrupt the speed, magnitude and scope of product innovation. ScienceSage\textbf{ScienceSage} enables researchers to build, store, update and query a knowledge base (KB). A KB codifies user's knowledge/information of a given domain in both vector index and knowledge graph (KG) index for efficient information retrieval and query. The knowledge/information can be extracted from user's textual documents, images, videos, audios and/or the research reports generated based on a research question and the latest relevant information on internet. The same set of KBs interconnect three functions on ScienceSage\textbf{ScienceSage}: 'Generate Research Report', 'Chat With Your Documents' and 'Chat With Anything'. We share our learning to encourage discussion and improvement of GenAI's role in scientific research.

Keywords

Cite

@article{arxiv.2502.18479,
  title  = {Disrupt Your Research Using Generative AI Powered ScienceSage},
  author = {Yong Zhang and Eric Herrison Gyamfi and Kelly Anderson and Sasha Roberts and Matt Barker},
  journal= {arXiv preprint arXiv:2502.18479},
  year   = {2025}
}

Comments

This paper has been accepted by Workshop of Deployable AI at AAAI 2025

R2 v1 2026-06-28T21:57:43.512Z