Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining |
| Seiten | 1044-1047 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9798400713293 |
| Publikationsstatus | Veröffentlicht - 10 März 2025 |
| Veranstaltung | 18th ACM International Conference on Web Search and Data Mining, WSDM 2025 - Hannover, Deutschland Dauer: 10 März 2025 → 14 März 2025 |
Publikationsreihe
| Name | WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining |
|---|
Abstract
Managing research digital objects (RDOs) in compliance with FAIR principles is crucial for ensuring accessibility, interoperability, and reusability across scientific domains. The Leibniz Data Manager (LDM) is a state-of-the-art framework that integrates Knowledge Graphs (KGs) and Neuro-Symbolic AI, combining the reasoning power of Large Language Models (LLMs) with structured metadata. LDM supports the management and enhancement of RDOs through entity linking, connecting datasets to external KGs like Wikidata and the Open Research Knowledge Graph (ORKG). Additionally, LDM offers federated query processing across KGs, enabling users to explore related papers, datasets, and resources through natural language questions. This demo showcases LDM's capabilities to explore RDOs, compare existing datasets, and extend metadata. By blending Neuro-Symbolic AI with FAIR and federated research data management, LDM offers a powerful tool for accelerating data-driven discovery in science. LDM is publicly accessible at https://service.tib.eu/ldmservice/.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Software
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- Apa
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- BibTex
- RIS
WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining. 2025. S. 1044-1047 (WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Integrating Knowledge Graphs and Neuro-Symbolic AI
T2 - 18th ACM International Conference on Web Search and Data Mining, WSDM 2025
AU - Sakor, Ahmad
AU - Brunet, Mauricio
AU - Iglesias, Enrique
AU - Rivas, Ariam
AU - Rohde, Philipp D.
AU - Kraft, Angelina
AU - Vidal, Maria Esther
N1 - Publisher Copyright: © 2025 Copyright held by the owner/author(s).
PY - 2025/3/10
Y1 - 2025/3/10
N2 - Managing research digital objects (RDOs) in compliance with FAIR principles is crucial for ensuring accessibility, interoperability, and reusability across scientific domains. The Leibniz Data Manager (LDM) is a state-of-the-art framework that integrates Knowledge Graphs (KGs) and Neuro-Symbolic AI, combining the reasoning power of Large Language Models (LLMs) with structured metadata. LDM supports the management and enhancement of RDOs through entity linking, connecting datasets to external KGs like Wikidata and the Open Research Knowledge Graph (ORKG). Additionally, LDM offers federated query processing across KGs, enabling users to explore related papers, datasets, and resources through natural language questions. This demo showcases LDM's capabilities to explore RDOs, compare existing datasets, and extend metadata. By blending Neuro-Symbolic AI with FAIR and federated research data management, LDM offers a powerful tool for accelerating data-driven discovery in science. LDM is publicly accessible at https://service.tib.eu/ldmservice/.
AB - Managing research digital objects (RDOs) in compliance with FAIR principles is crucial for ensuring accessibility, interoperability, and reusability across scientific domains. The Leibniz Data Manager (LDM) is a state-of-the-art framework that integrates Knowledge Graphs (KGs) and Neuro-Symbolic AI, combining the reasoning power of Large Language Models (LLMs) with structured metadata. LDM supports the management and enhancement of RDOs through entity linking, connecting datasets to external KGs like Wikidata and the Open Research Knowledge Graph (ORKG). Additionally, LDM offers federated query processing across KGs, enabling users to explore related papers, datasets, and resources through natural language questions. This demo showcases LDM's capabilities to explore RDOs, compare existing datasets, and extend metadata. By blending Neuro-Symbolic AI with FAIR and federated research data management, LDM offers a powerful tool for accelerating data-driven discovery in science. LDM is publicly accessible at https://service.tib.eu/ldmservice/.
KW - Data Science
KW - Digital Repositories
KW - Federated Search
UR - http://www.scopus.com/inward/record.url?scp=105001674633&partnerID=8YFLogxK
U2 - 10.1145/3701551.3704125
DO - 10.1145/3701551.3704125
M3 - Conference contribution
AN - SCOPUS:105001674633
T3 - WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining
SP - 1044
EP - 1047
BT - WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining
Y2 - 10 March 2025 through 14 March 2025
ER -