Preface for LLMs4OL 2024: The 1st Large Language Models for Ontology Learning Challenge at the 23rd ISWC

We are pleased to present the proceedings of the “1st Large Language Models for Ontology Learning Challenge (LLMs4OL 2024)”, held at the 23rd International Semantic Web Conference (ISWC). This challenge marks a significant a dvancement in utilizing Large Language Models (LLMs) for Ontology Learning (OL)—a key Semantic Web component that facilitates the automatic extraction of structured knowledge from unstructured data. The challenge features three main tasks: Term Typing (identifying categories for terms), Taxonomy Discovery (uncovering hierarchical relationships), and Non-Taxonomic Relation Extraction (identifying other meaningful relationships between terms). Each task is designed to test different facets of ontology construction and to encourage the exploration of innovative techniques. This challenge seeks to foster collaboration, inspire innovation, and expand the capabilities of LLMs in OL. The proceedings include a collection of innovative solutions and insights from global participants, highlighting the crucial role of LLMs in enhancing the web with structured knowledge. We believe the outcomes of this challenge will propel further advancements in OL and its applications on the semantic web. We would like to extend our gratitude to all the participants for their invaluable contributions, which their solutions and dedication have greatly enriched this challenge. Our sincere thanks also go to the conference organizers and committee for their efforts in hosting this event. We are deeply appreciative of the reviewers for their evaluations and feedback. Their reviews have been instrumental in enhancing the quality of the submissions. We would like to specifically acknowledge: Dr. Amin Keshavarzi (Postdoctoral Researcher, L3S & TIB, Germany) Mostafa Rahgouy (Lead Project Coordinator, Auburn University, USA) Sahar Tahmasebi (Doctoral Researcher, TIB, Germany) Milad Molazadeh Oskuee (Lead NLP Researcher, Iran) Aida Usmanova (Doctoral Researcher, Leuphana Universit ¨ at L¨uneburg, Germany) Emetis Niazmand (Doctoral Researcher, TIB, Germany) Lastly, we would like to express our gratitude to ”Xenia Felice van Edig” and ”Karolina Linerová” from the TIB Open Publishing, for their support in bringing these proceedings online. We also would like to acknowledge that the 1st LLMs4OL Challenge @ ISWC 2024 jointly supported by the NFDI4DataScience initiative (DFG, German Research Foundation, Grant ID: 460234259) and the SCINEXT project (BMBF, German Federal Ministry of Education and Research, Grant ID: 01lS22070).

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Publication Details

Journal
Open Conference Proceedings
Published
2024-10-02
DOI
https://doi.org/10.52825/ocp.v4i.2472
Citations
9
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
1.35

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article

Preface for LLMs4OL 2024: The 1st Large Language Models for Ontology Learning Challenge at the 23rd ISWC

Hamed Babaei Giglou, Jennifer D’Souza, Sören Auer
9 citations
Open Conference Proceedings
Semantic Web and Ontologies
1.35
article

Preface for LLMs4OL 2024: The 1st Large Language Models for Ontology Learning Challenge at the 23rd ISWC

Hamed Babaei Giglou, Jennifer D’Souza, Sören Auer
article en
9 citations

Abstract

We are pleased to present the proceedings of the “1st Large Language Models for Ontology Learning Challenge (LLMs4OL 2024)”, held at the 23rd International Semantic Web Conference (ISWC). This challenge marks a significant a dvancement in utilizing Large Language Models (LLMs) for Ontology Learning (OL)—a key Semantic Web component that facilitates the automatic extraction of structured knowledge from unstructured data. The challenge features three main tasks: Term Typing (identifying categories for terms), Taxonomy Discovery (uncovering hierarchical relationships), and Non-Taxonomic Relation Extraction (identifying other meaningful relationships between terms). Each task is designed to test different facets of ontology construction and to encourage the exploration of innovative techniques. This challenge seeks to foster collaboration, inspire innovation, and expand the capabilities of LLMs in OL. The proceedings include a collection of innovative solutions and insights from global participants, highlighting the crucial role of LLMs in enhancing the web with structured knowledge. We believe the outcomes of this challenge will propel further advancements in OL and its applications on the semantic web. We would like to extend our gratitude to all the participants for their invaluable contributions, which their solutions and dedication have greatly enriched this challenge. Our sincere thanks also go to the conference organizers and committee for their efforts in hosting this event. We are deeply appreciative of the reviewers for their evaluations and feedback. Their reviews have been instrumental in enhancing the quality of the submissions. We would like to specifically acknowledge: Dr. Amin Keshavarzi (Postdoctoral Researcher, L3S & TIB, Germany) Mostafa Rahgouy (Lead Project Coordinator, Auburn University, USA) Sahar Tahmasebi (Doctoral Researcher, TIB, Germany) Milad Molazadeh Oskuee (Lead NLP Researcher, Iran) Aida Usmanova (Doctoral Researcher, Leuphana Universit ¨ at L¨uneburg, Germany) Emetis Niazmand (Doctoral Researcher, TIB, Germany) Lastly, we would like to express our gratitude to ”Xenia Felice van Edig” and ”Karolina Linerová” from the TIB Open Publishing, for their support in bringing these proceedings online. We also would like to acknowledge that the 1st LLMs4OL Challenge @ ISWC 2024 jointly supported by the NFDI4DataScience initiative (DFG, German Research Foundation, Grant ID: 460234259) and the SCINEXT project (BMBF, German Federal Ministry of Education and Research, Grant ID: 01lS22070).

Open Conference ProceedingsVol. 4
Technische Informationsbibliothek (TIB) (DE)
Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung
Industry, innovation and infrastructure
Openalex Percentile: Top 18%
Semantic Web and Ontologies
1.35
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