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An Empirical Study of Learning to Rank for Entity Search

Published: 07 July 2016 Publication History

Abstract

This work investigates the effectiveness of learning to rank methods for entity search. Entities are represented by multi-field documents constructed from their RDF triples, and field-based text similarity features are extracted for query-entity pairs. State-of-the-art learning to rank methods learn models for ad-hoc entity search. Our experiments on an entity search test collection based on DBpedia confirm that learning to rank methods are as powerful for ranking entities as for ranking documents, and establish a new state-of-the-art for accuracy on this benchmark dataset.

References

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K. Balog and R. Neumayer. A test collection for entity search in dbpedia. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2013), pages 737{740. ACM, 2013.
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C. Lu, W. Lam, and Y. Liao. Entity retrieval via entity factoid hierarchy. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015), pages 514--523. ACL, 2015.
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J. Pound, P. Mika, and H. Zaragoza. Ad-hoc object retrieval in the web of data. In Proceedings of the 19th international conference on World wide web (WWW 2010), pages 771--780. ACM, 2010.
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C. Xiong and J. Callan. Esdrank: Connecting query and documents through external semi-structured data. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM 2015), pages 951--960. ACM, 2015.
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C. Xiong and J. Callan. Query expansion with freebase. In Proceedings of the 2015 International Conference on The Theory of Information Retrieval (ICTIR 2015), pages 111--120. ACM, 2015.
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N. Zhiltsov, A. Kotov, and F. Nikolaev. Fielded sequential dependence model for ad-hoc entity retrieval in the web of data. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2015), pages 253--262. ACM, 2015.

Cited By

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  • (2025)Knowledge graph based entity selection framework for ad-hoc retrievalWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2024.10084884:COnline publication date: 18-Feb-2025
  • (2024)Benchmark and Neural Architecture for Conversational Entity Retrieval from a Knowledge GraphProceedings of the ACM Web Conference 202410.1145/3589334.3645676(1519-1528)Online publication date: 13-May-2024
  • (2024)Learning contextual representations for entity retrievalApplied Intelligence10.1007/s10489-024-05430-054:19(8820-8840)Online publication date: 4-Jul-2024
  • Show More Cited By

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Published In

cover image ACM Conferences
SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
July 2016
1296 pages
ISBN:9781450340694
DOI:10.1145/2911451
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2016

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Author Tags

  1. dbpedia
  2. entity search
  3. knowledge base
  4. learning to rank

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  • Short-paper

Funding Sources

  • United States National Science Foundation

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SIGIR '16
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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2025)Knowledge graph based entity selection framework for ad-hoc retrievalWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2024.10084884:COnline publication date: 18-Feb-2025
  • (2024)Benchmark and Neural Architecture for Conversational Entity Retrieval from a Knowledge GraphProceedings of the ACM Web Conference 202410.1145/3589334.3645676(1519-1528)Online publication date: 13-May-2024
  • (2024)Learning contextual representations for entity retrievalApplied Intelligence10.1007/s10489-024-05430-054:19(8820-8840)Online publication date: 4-Jul-2024
  • (2023)Field featuresApplied Soft Computing10.1016/j.asoc.2023.110183138:COnline publication date: 1-May-2023
  • (2022)Active tag recommendation for interactive entity searchInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10285659:2Online publication date: 1-Mar-2022
  • (2022)An Entity-Oriented Approach for Answering Topical Information NeedsAdvances in Information Retrieval10.1007/978-3-030-99739-7_57(463-472)Online publication date: 10-Apr-2022
  • (2021)Extraction of Effective Textual and Semantic Features in Learning to Rank for Web Document RetrievalIranian Journal of Information Processing and Management10.52547/jipm.36.4.108136:4(1081-1112)Online publication date: 1-Jul-2021
  • (2021)Semantic Table Retrieval Using Keyword and Table QueriesACM Transactions on the Web10.1145/344169015:3(1-33)Online publication date: 13-May-2021
  • (2021)WTR: A Test Collection for Web Table RetrievalProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463260(2514-2520)Online publication date: 11-Jul-2021
  • (2021)Improving Discriminative Entity Retriever with Generative Tasks2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT)10.1109/CECIT53797.2021.00120(656-660)Online publication date: Dec-2021
  • Show More Cited By

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