research-article

ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval

Authors:

Qiaosheng Chen,

Evgeny KharlamovAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2981 - 2991

https://doi.org/10.1145/3477495.3531729

Published: 07 July 2022 Publication History

Abstract

Ad hoc dataset retrieval is a trending topic in IR research. Methods and systems are evolving from metadata-based to content-based ones which exploit the data itself for improving retrieval accuracy but thus far lack a specialized test collection. In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts who are assisted with a dashboard designed specifically for comprehensively and conveniently browsing both the metadata and data of a dataset. We conduct extensive experiments on the test collection to analyze its difficulty and provide insights into the underlying task.

Supplementary Material

MP4 File (SIGIR22-rs1336.mp4)

Ad hoc dataset retrieval methods are evolving from metadata-based to content-based ones which exploit the data itself but thus far lack a specialized test collection. In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts. In particular, we designed a dashboard to help experts to comprehensively and conveniently browse both the metadata and large data of datasets. Finally, we evaluate four retrieval models TF-IDF, BM25F, FSDM, and LMD on the test collection.

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Cited By

Chen QLuo WHuang ZLin TWang XSoylu AEll BZhou BKharlamov ECheng GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ACORDAR 2.0: A Test Collection for Ad Hoc Dataset Retrieval with Densely Pooled Datasets and Question-Style QueriesProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657866(303-312)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657866
Chen QChen JZhou XCheng GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Enhancing Dataset Search with Compact Data SnippetsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657837(1093-1103)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657837
Fujita YHayashi TKuwahara M(2024)Inferring Relationships between Tabular Data and Topics using LLM for a Dataset Search Task2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10826033(6564-6573)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10826033
Show More Cited By

Index Terms

ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
  2. World Wide Web
    1. Web data description languages
      1. Semantic web description languages
        Resource Description Framework (RDF)

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
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UAM and Amazon
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Julio Gonzalo
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Program Chairs:
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J. Shane Culpepper
RMIT University
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Gabriella Kazai
Waseda University

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

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Cited By

Chen QLuo WHuang ZLin TWang XSoylu AEll BZhou BKharlamov ECheng GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ACORDAR 2.0: A Test Collection for Ad Hoc Dataset Retrieval with Densely Pooled Datasets and Question-Style QueriesProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657866(303-312)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657866
Chen QChen JZhou XCheng GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Enhancing Dataset Search with Compact Data SnippetsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657837(1093-1103)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657837
Fujita YHayashi TKuwahara M(2024)Inferring Relationships between Tabular Data and Topics using LLM for a Dataset Search Task2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10826033(6564-6573)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10826033
Chen QZhou XZhang ZCheng G(2024)DUNKS: Chunking and Summarizing Large and Heterogeneous Data for Dataset SearchThe Semantic Web – ISWC 202410.1007/978-3-031-77850-6_5(78-97)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77850-6_5
Menotti LBarusco MForzan RSilvello G(2024)Content-Based Dataset Retrieval Methods: Reproducibility of the ACORDAR Test CollectionLinking Theory and Practice of Digital Libraries10.1007/978-3-031-72437-4_18(310-325)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72437-4_18
Chen QShi QCheng G(2023)Dataset Search over Integrated Metadata from China’s Public Data Open PlatformsBig Data10.1007/978-981-99-8979-9_3(32-43)Online publication date: 15-Dec-2023
https://doi.org/10.1007/978-981-99-8979-9_3
Chen QHuang ZZhang ZLuo WLin TShi QCheng G(2023)Dense Re-Ranking with Weak Supervision for RDF Dataset SearchThe Semantic Web – ISWC 202310.1007/978-3-031-47240-4_2(23-40)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47240-4_2

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