Introducing ORKG ASK: an AI-driven Scholarly Literature Search and Exploration System Taking a Neuro-Symbolic Approach

TL;DR

ASK is an AI-driven system that combines neuro-symbolic methods, vector search, LLMs, and knowledge graphs to enhance scholarly literature search and exploration, supporting researchers with natural language queries and automated information extraction.

Contribution

The paper introduces ASK, a novel neuro-symbolic AI system integrating multiple advanced techniques for improved scholarly literature search and question answering.

Findings

System is user-friendly and easy to use.

Users report high satisfaction with ASK.

ASK effectively retrieves relevant literature and answers research questions.

Abstract

As the volume of published scholarly literature continues to grow, finding relevant literature becomes increasingly difficult. With the rise of generative Artificial Intelligence (AI), and particularly Large Language Models (LLMs), new possibilities emerge to find and explore literature. We introduce ASK (Assistant for Scientific Knowledge), an AI-driven scholarly literature search and exploration system that follows a neuro-symbolic approach. ASK aims to provide active support to researchers in finding relevant scholarly literature by leveraging vector search, LLMs, and knowledge graphs. The system allows users to input research questions in natural language and retrieve relevant articles. ASK automatically extracts key information and generates answers to research questions using a Retrieval-Augmented Generation (RAG) approach. We present an evaluation of ASK, assessing the system's usability and usefulness. Findings indicate that the system is user-friendly and users are generally satisfied while using the system.