Details
| Original language | English |
|---|---|
| Title of host publication | ECAI 2024 - 27th European Conference on Artificial Intelligence, Including 13th Conference on Prestigious Applications of Intelligent Systems, PAIS 2024, Proceedings |
| Editors | Ulle Endriss, Francisco S. Melo, Kerstin Bach, Alberto Bugarin-Diz, Jose M. Alonso-Moral, Senen Barro, Fredrik Heintz |
| Pages | 1238-1245 |
| Number of pages | 8 |
| ISBN (electronic) | 9781643685489 |
| Publication status | Published - 19 Oct 2024 |
| Event | 27th European Conference on Artificial Intelligence, ECAI 2024 - Santiago de Compostela, Spain Duration: 19 Oct 2024 → 24 Oct 2024 |
Publication series
| Name | Frontiers in Artificial Intelligence and Applications |
|---|---|
| Volume | 392 |
| ISSN (Print) | 0922-6389 |
| ISSN (electronic) | 1879-8314 |
Abstract
Academic Search Engines (ASEs) are crucial for navigating the vast landscape of scholarly literature. Traditionally, these engines rely on keyword-based search, supplemented by predefined facets encompassing metadata such as research field, publication year, type, authors, and language. However, ASEs are limited in their ability to generate dynamic facets in real-time based on article contents. This limitation impedes the efficient exploration and navigation of large article collections. We propose an approach that addresses this limitation by dynamically generating facets using article abstracts. We introduce three distinct methods for dynamic facet generation: (1) KB2 (based on Knowledge Bases) utilizes two knowledge bases (KB) to extract facet values and their associated facets; (2) KBLLM (based on a Knowledge Base and a Large Language Model) utilizes a KB for extracting facet values and a large language model (LLM) to categorize these values by predicting facets; finally, (3) KBLLMKA (based on a Knowledge Base and a Large Language Model with Knowledge Augmentation) combines KB-spotting with facet-value pair extraction and adds this information as auxiliary data to enhance LLM's facet prediction capabilities. We evaluated the effectiveness of these methods with a user study, performance evaluation, and comparative analyses, which showed the effectiveness of the approach.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
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ECAI 2024 - 27th European Conference on Artificial Intelligence, Including 13th Conference on Prestigious Applications of Intelligent Systems, PAIS 2024, Proceedings. ed. / Ulle Endriss; Francisco S. Melo; Kerstin Bach; Alberto Bugarin-Diz; Jose M. Alonso-Moral; Senen Barro; Fredrik Heintz. 2024. p. 1238-1245 (Frontiers in Artificial Intelligence and Applications; Vol. 392).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Neuro-Symbolic Approach for Faceted Search in Digital Libraries
AU - Khalid, Mutahira
AU - Auer, Sören
AU - Stocker, Markus
N1 - Publisher Copyright: © 2024 The Authors.
PY - 2024/10/19
Y1 - 2024/10/19
N2 - Academic Search Engines (ASEs) are crucial for navigating the vast landscape of scholarly literature. Traditionally, these engines rely on keyword-based search, supplemented by predefined facets encompassing metadata such as research field, publication year, type, authors, and language. However, ASEs are limited in their ability to generate dynamic facets in real-time based on article contents. This limitation impedes the efficient exploration and navigation of large article collections. We propose an approach that addresses this limitation by dynamically generating facets using article abstracts. We introduce three distinct methods for dynamic facet generation: (1) KB2 (based on Knowledge Bases) utilizes two knowledge bases (KB) to extract facet values and their associated facets; (2) KBLLM (based on a Knowledge Base and a Large Language Model) utilizes a KB for extracting facet values and a large language model (LLM) to categorize these values by predicting facets; finally, (3) KBLLMKA (based on a Knowledge Base and a Large Language Model with Knowledge Augmentation) combines KB-spotting with facet-value pair extraction and adds this information as auxiliary data to enhance LLM's facet prediction capabilities. We evaluated the effectiveness of these methods with a user study, performance evaluation, and comparative analyses, which showed the effectiveness of the approach.
AB - Academic Search Engines (ASEs) are crucial for navigating the vast landscape of scholarly literature. Traditionally, these engines rely on keyword-based search, supplemented by predefined facets encompassing metadata such as research field, publication year, type, authors, and language. However, ASEs are limited in their ability to generate dynamic facets in real-time based on article contents. This limitation impedes the efficient exploration and navigation of large article collections. We propose an approach that addresses this limitation by dynamically generating facets using article abstracts. We introduce three distinct methods for dynamic facet generation: (1) KB2 (based on Knowledge Bases) utilizes two knowledge bases (KB) to extract facet values and their associated facets; (2) KBLLM (based on a Knowledge Base and a Large Language Model) utilizes a KB for extracting facet values and a large language model (LLM) to categorize these values by predicting facets; finally, (3) KBLLMKA (based on a Knowledge Base and a Large Language Model with Knowledge Augmentation) combines KB-spotting with facet-value pair extraction and adds this information as auxiliary data to enhance LLM's facet prediction capabilities. We evaluated the effectiveness of these methods with a user study, performance evaluation, and comparative analyses, which showed the effectiveness of the approach.
UR - http://www.scopus.com/inward/record.url?scp=85213351857&partnerID=8YFLogxK
U2 - 10.3233/FAIA240620
DO - 10.3233/FAIA240620
M3 - Conference contribution
AN - SCOPUS:85213351857
T3 - Frontiers in Artificial Intelligence and Applications
SP - 1238
EP - 1245
BT - ECAI 2024 - 27th European Conference on Artificial Intelligence, Including 13th Conference on Prestigious Applications of Intelligent Systems, PAIS 2024, Proceedings
A2 - Endriss, Ulle
A2 - Melo, Francisco S.
A2 - Bach, Kerstin
A2 - Bugarin-Diz, Alberto
A2 - Alonso-Moral, Jose M.
A2 - Barro, Senen
A2 - Heintz, Fredrik
T2 - 27th European Conference on Artificial Intelligence, ECAI 2024
Y2 - 19 October 2024 through 24 October 2024
ER -