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

TL;DR

ORKG ASK is a neuro-symbolic system that combines semantic search, LLMs, and knowledge graphs to improve scholarly article retrieval and exploration, making scientific literature more accessible and easier to navigate.

Contribution

The paper introduces ORKG ASK, a novel neuro-symbolic scholarly search system that integrates advanced AI components for effective literature retrieval and exploration.

Findings

System provides relevant article sets for user queries

Preliminary evaluation shows effective scholarly information retrieval

Open source components facilitate community adoption

Abstract

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.