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

• URL: http://arxiv.org/abs/2512.16425v1
• Date: Thu, 18 Dec 2025 11:25:14 GMT
• Title: Introducing ORKG ASK: an AI-driven Scholarly Literature Search and Exploration System Taking a Neuro-Symbolic Approach
• Authors: Allard Oelen, Mohamad Yaser Jaradeh, Sören Auer,
• Abstract summary: ASK (Assistant for Scientific Knowledge) is an AI-driven scholarly literature search and exploration system.<n>The system allows users to input research questions in natural language and retrieve relevant articles.<n>It automatically extracts key information and generates answers to research questions using a Retrieval-Augmented Generation (RAG) approach.
• Score: 4.4684259220459035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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