TY - GEN

T1 - Neuro-Symbolic Federated Research Artifact Search

AU - Keya, Farhana

AU - Auer, Sören

AU - Jaradeh, Mohamad Yaser

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026/9/15

Y1 - 2026/9/15

N2 - Scientific research artifacts such as datasets, software or ontologies are essential components of scientific discovery. Yet, the growing volume of such artifacts requires more efficient and relevant search and retrieval systems. We present a neuro-symbolic approach for federated research artifact search, specifically for datasets and software metadata over Resodate and Wikidata. Integrated into the ORKG ASK platform, our system processes user queries through linguistic analysis to extract key terms. These key terms are then used to retrieve and recommend relevant research artifacts from federated sources, ensuring precise and contextually relevant metadata discovery. To further enhance retrieval accuracy, we employ a ranking mechanism that organizes research artifacts based on each user query’s structure and morphological features. We evaluate various key-term extraction methods and ranking approaches, integrating both symbolic and neural techniques. We rigorously evaluate the key-term extraction using Precision, Recall, and F1-score, and assess the re-ranking effectiveness by comparing with human rankings through correlation metrics and LLM-based evaluations. Our experiments show that symbolic methods outperform the neural approach regarding accuracy and response time. As a result, our system offers users more effective and efficient research artifact recommendations.

AB - Scientific research artifacts such as datasets, software or ontologies are essential components of scientific discovery. Yet, the growing volume of such artifacts requires more efficient and relevant search and retrieval systems. We present a neuro-symbolic approach for federated research artifact search, specifically for datasets and software metadata over Resodate and Wikidata. Integrated into the ORKG ASK platform, our system processes user queries through linguistic analysis to extract key terms. These key terms are then used to retrieve and recommend relevant research artifacts from federated sources, ensuring precise and contextually relevant metadata discovery. To further enhance retrieval accuracy, we employ a ranking mechanism that organizes research artifacts based on each user query’s structure and morphological features. We evaluate various key-term extraction methods and ranking approaches, integrating both symbolic and neural techniques. We rigorously evaluate the key-term extraction using Precision, Recall, and F1-score, and assess the re-ranking effectiveness by comparing with human rankings through correlation metrics and LLM-based evaluations. Our experiments show that symbolic methods outperform the neural approach regarding accuracy and response time. As a result, our system offers users more effective and efficient research artifact recommendations.

KW - Federated Search

KW - Key Term Extraction

KW - Neuro-Symbolic Systems

UR - http://www.scopus.com/inward/record.url?scp=105018309166&partnerID=8YFLogxK

U2 - 10.1007/978-3-032-05409-8_10

DO - 10.1007/978-3-032-05409-8_10

M3 - Conference contribution

AN - SCOPUS:105018309166

SN - 9783032054081

T3 - Lecture Notes in Computer Science

SP - 145

EP - 162

BT - Linking Theory and Practice of Digital Libraries - 29th International Conference on Theory and Practice of Digital Libraries, TPDL 2025, Proceedings

A2 - Balke, Wolf-Tilo

A2 - Golub, Koraljka

A2 - Manolopoulos, Yannis

A2 - Stefanidis, Kostas

A2 - Zhang, Zheying

PB - Springer Science and Business Media Deutschland GmbH

T2 - 29th International Conference on Theory and Practice of Digital Libraries, TPDL 2025

Y2 - 23 September 2025 through 26 September 2025

ER -