# ORKG ASK: a Neuro-symbolic Scholarly Search and Exploration System

arXiv: https://arxiv.org/abs/2412.04977 · HTML: https://mlsanity.com/paper/2412.04977/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2024.

## Built on by (most cited first, 3 of 3)

- [DeepResearchEco: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology](https://mlsanity.com/paper/2507.10522/) (2025)
- [Introducing ORKG ASK: an AI-driven Scholarly Literature Search and Exploration System Taking a Neuro-Symbolic Approach](https://mlsanity.com/paper/2512.16425/) (2025)
- [TIB AIssistant: a Platform for AI-Supported Research Across Research Life Cycles](https://mlsanity.com/paper/2512.16442/) (2025)

## Abstract (arXiv)

Purpose: Finding scholarly articles is a time-consuming and cumbersome activity, yet crucial for conducting science. Due to the growing number of scholarly articles, new scholarly search systems are needed to effectively assist researchers in finding relevant literature. Methodology: We take a neuro-symbolic approach to scholarly search and exploration by leveraging state-of-the-art components, including semantic search, Large Language Models (LLMs), and Knowledge Graphs (KGs). The semantic search component composes a set of relevant articles. From this set of articles, information is extracted and presented to the user. Findings: The presented system, called ORKG ASK (Assistant for Scientific Knowledge), provides a production-ready search and exploration system. Our preliminary evaluation indicates that our proposed approach is indeed suitable for the task of scholarly information retrieval. Value: With ORKG ASK, we present a next-generation scholarly search and exploration system and make it available online. Additionally, the system components are open source with a permissive license.