2024/12/06 by Allard Oelen, Oelen, Allard, Mohamad Yaser Jaradeh +3 · 6 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #Digital Libraries (cs.DL) #FOS: Computer and information sciences #Semantic Web and Ontologies #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2412.04977
openalex publication_date 2024/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.