Details
| Original language | English |
|---|---|
| Journal | CEUR Workshop Proceedings |
| Volume | 3759 |
| Publication status | Published - 2024 |
| Event | Joint of Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems, SEMANTiCS-PDWT 2024 - Amsterdam, Netherlands Duration: 17 Sept 2024 → 19 Sept 2024 |
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.
Keywords
- Large Language Models, Neuro-symbolic AI, Scholarly Knowledge Graphs, Scholarly Search System
ASJC Scopus subject areas
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In: CEUR Workshop Proceedings, Vol. 3759, 2024.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - ORKG ASK
T2 - Joint of Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems, SEMANTiCS-PDWT 2024
AU - Oelen, Allard
AU - Jaradeh, Mohamad Yaser
AU - Auer, Sören
N1 - Publisher Copyright: © 2024 Copyright for this paper by its authors.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Large Language Models
KW - Neuro-symbolic AI
KW - Scholarly Knowledge Graphs
KW - Scholarly Search System
UR - http://www.scopus.com/inward/record.url?scp=85204701963&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85204701963
VL - 3759
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
Y2 - 17 September 2024 through 19 September 2024
ER -