Details
| Original language | English |
|---|---|
| Title of host publication | Web Engineering - 25th International Conference, ICWE 2025, Proceedings |
| Editors | Himanshu Verma, Alessandro Bozzon, Jie Yang, Andrea Mauri |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 11-25 |
| Number of pages | 15 |
| ISBN (electronic) | 978-3-031-97207-2 |
| ISBN (print) | 9783031972065 |
| Publication status | Published - 12 Oct 2025 |
| Event | 25th International Conference on Web Engineering, ICWE 2025 - Delft, Netherlands Duration: 30 Jun 2025 → 3 Jul 2025 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Volume | 15749 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
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.
Keywords
- AI-Supported Digital Library, Intelligent User Interface, Large Language Models, Scholarly Search System
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Web Engineering - 25th International Conference, ICWE 2025, Proceedings. ed. / Himanshu Verma; Alessandro Bozzon; Jie Yang; Andrea Mauri. Springer Science and Business Media Deutschland GmbH, 2025. p. 11-25 (Lecture Notes in Computer Science; Vol. 15749 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
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TY - GEN
T1 - Introducing ORKG ASK
T2 - 25th International Conference on Web Engineering, ICWE 2025
AU - Oelen, Allard
AU - Jaradeh, Mohamad Yaser
AU - Auer, Sören
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2025/10/12
Y1 - 2025/10/12
N2 - 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.
AB - 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.
KW - AI-Supported Digital Library
KW - Intelligent User Interface
KW - Large Language Models
KW - Scholarly Search System
UR - http://www.scopus.com/inward/record.url?scp=105020022356&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-97207-2_2
DO - 10.1007/978-3-031-97207-2_2
M3 - Conference contribution
AN - SCOPUS:105020022356
SN - 9783031972065
T3 - Lecture Notes in Computer Science
SP - 11
EP - 25
BT - Web Engineering - 25th International Conference, ICWE 2025, Proceedings
A2 - Verma, Himanshu
A2 - Bozzon, Alessandro
A2 - Yang, Jie
A2 - Mauri, Andrea
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 30 June 2025 through 3 July 2025
ER -