short-paper

Entity Retrieval Using Fine-Grained Entity Aspects

Authors:

Shubham Chatterjee,

Laura DietzAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1662 - 1666

https://doi.org/10.1145/3404835.3463035

Published: 11 July 2021 Publication History

Abstract

Using entity aspect links, we improve upon the current state-of-the-art in entity retrieval. Entity retrieval is the task of retrieving relevant entities for search queries, such as "Antibiotic Use In Livestock". Entity aspect linking is a new technique to refine the semantic information of entity links. For example, while passages relevant to the query above may mention the entity "USA", there are many aspects of the USA of which only few, such as "USA/Agriculture", are relevant for this query. By using entity aspect links that indicate which aspect of an entity is being referred to in the context of the query, we obtain more specific relevance indicators for entities. We show that our approach improves upon all baseline methods, including the current state-of-the-art using a standard entity retrieval test collection. With this work, we release a large collection of entity-aspect-links for a large TREC corpus.

Supplementary Material

MP4 File (SIGIR-2021-Talk-Chatterjee-Shubham.mp4)

Presentation Video

Download
12.28 MB

References

[1]

Krisztian Balog, Marc Bron, and Maarten De Rijke. 2011. Query Modeling for Entity Search Based on Terms, Categories, and Examples. ACM Trans. Inf. Syst., Vol. 29, 4, Article 22 (Dec. 2011), 31 pages. https://doi.org/10.1145/2037661.2037667

Digital Library

[2]

Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (Vancouver, Canada) (SIGMOD '08). Association for Computing Machinery, New York, NY, USA, 1247--1250. https://doi.org/10.1145/1376616.1376746

Digital Library

[3]

Shubham Chatterjee and Laura Dietz. 2019. Why Does This Entity Matter? Support Passage Retrieval for Entity Retrieval. In Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval (Santa Clara, CA, USA) (ICTIR '19). Association for Computing Machinery, New York, NY, USA, 221--224. https://doi.org/10.1145/3341981.3344243

Digital Library

[4]

Jeffrey Dalton, Laura Dietz, and James Allan. 2014. Entity Query Feature Expansion Using Knowledge Base Links. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (Gold Coast, Queensland, Australia) (SIGIR '14). Association for Computing Machinery, New York, NY, USA, 365--374. https://doi.org/10.1145/2600428.2609628

Digital Library

[5]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186. https://doi.org/10.18653/v1/N19--1423

[6]

Laura Dietz. 2019. ENT Rank: Retrieving Entities for Topical Information Needs through Entity-Neighbor-Text Relations. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 215--224. https://doi.org/10.1145/3331184.3331257

Digital Library

[7]

Laura Dietz and John Foley. 2019. TREC CAR Y3: Complex Answer Retrieval Overview. In Proceedings of Text REtrieval Conference (TREC) .

[8]

Paolo Ferragina and Ugo Scaiella. 2010. TAGME: On-the-Fly Annotation of Short Text Fragments (by Wikipedia Entities). In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (Toronto, ON, Canada) (CIKM '10). Association for Computing Machinery, New York, NY, USA, 1625--1628. https://doi.org/10.1145/1871437.1871689

Digital Library

[9]

Dar'io Garigliotti and Krisztian Balog. 2017. On Type-Aware Entity Retrieval. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval (Amsterdam, The Netherlands) (ICTIR '17). Association for Computing Machinery, New York, NY, USA, 27--34. https://doi.org/10.1145/3121050.3121054

Digital Library

[10]

Emma J Gerritse, Faegheh Hasibi, and Arjen P de Vries. 2020. Graph-Embedding Empowered Entity Retrieval. In European Conference on Information Retrieval. Springer, 97--110.

[11]

David Graus, Manos Tsagkias, Wouter Weerkamp, Edgar Meij, and Maarten de Rijke. 2016. Dynamic Collective Entity Representations for Entity Ranking. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (San Francisco, California, USA) (WSDM '16). Association for Computing Machinery, New York, NY, USA, 595--604. https://doi.org/10.1145/2835776.2835819

Digital Library

[12]

Jiafeng Guo, Gu Xu, Xueqi Cheng, and Hang Li. 2009. Named Entity Recognition in Query. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Boston, MA, USA) (SIGIR '09). Association for Computing Machinery, New York, NY, USA, 267--274. https://doi.org/10.1145/1571941.1571989

Digital Library

[13]

Faegheh Hasibi, Krisztian Balog, and Svein Erik Bratsberg. 2016. Exploiting Entity Linking in Queries for Entity Retrieval. In Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval (Newark, Delaware, USA) (ICTIR '16). Association for Computing Machinery, New York, NY, USA, 209--218. https://doi.org/10.1145/2970398.2970406

Digital Library

[14]

Rianne Kaptein, Pavel Serdyukov, Arjen De Vries, and Jaap Kamps. 2010. Entity Ranking Using Wikipedia as a Pivot. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (Toronto, ON, Canada) (CIKM '10). Association for Computing Machinery, New York, NY, USA, 69--78. https://doi.org/10.1145/1871437.1871451

Digital Library

[15]

Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi. 2017. Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4999--5007.

[16]

Victor Lavrenko and W. Bruce Croft. 2001. Relevance-Based Language Models. SIGIR Forum, Vol. 51, 2 (Aug. 2001), 260--267. https://doi.org/10.1145/3130348.3130376

Digital Library

[17]

Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015. Dbpedia--A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia. Semantic web, Vol. 6, 2 (2015), 167--195.

[18]

Thomas Lin, Patrick Pantel, Michael Gamon, Anitha Kannan, and Ariel Fuxman. 2012. Active Objects: Actions for Entity-Centric Search. In Proceedings of the 21st International Conference on World Wide Web (Lyon, France) (WWW '12). Association for Computing Machinery, New York, NY, USA, 589--598. https://doi.org/10.1145/2187836.2187916

Digital Library

[19]

Xitong Liu and Hui Fang. 2015. Latent Entity Space: A Novel Retrieval Approach for Entity-bearing Queries. Information Retrieval Journal, Vol. 18, 6 (2015), 473--503. https://doi.org/10.1007/s10791-015--9267-x

Digital Library

[20]

Edgar Meij, Marc Bron, Laura Hollink, Bouke Huurnink, and Maarten de Rijke. 2011. Mapping Queries to the Linking Open Data cloud: A case study using DBpedia. Journal of Web Semantics, Vol. 9, 4 (2011), 418 -- 433. https://doi.org/10.1016/j.websem.2011.04.001 JWS special issue on Semantic Search.

Digital Library

[21]

Donald Metzler and W. Bruce Croft. 2005. A Markov Random Field Model for Term Dependencies. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Salvador, Brazil) (SIGIR '05). Association for Computing Machinery, New York, NY, USA, 472--479. https://doi.org/10.1145/1076034.1076115

Digital Library

[22]

Federico Nanni, Simone Paolo Ponzetto, and Laura Dietz. 2018. Entity-Aspect Linking: Providing Fine-Grained Semantics of Entities in Context. In Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries (Fort Worth, Texas, USA) (JCDL '18). Association for Computing Machinery, New York, NY, USA, 49--58. https://doi.org/10.1145/3197026.3197047

Digital Library

[23]

Fedor Nikolaev, Alexander Kotov, and Nikita Zhiltsov. 2016. Parameterized Fielded Term Dependence Models for Ad-Hoc Entity Retrieval from Knowledge Graph. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (Pisa, Italy) (SIGIR '16). Association for Computing Machinery, New York, NY, USA, 435--444. https://doi.org/10.1145/2911451.2911545

Digital Library

[24]

Jeffrey Pound, Peter Mika, and Hugo Zaragoza. 2010. Ad-Hoc Object Retrieval in the Web of Data. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW '10). Association for Computing Machinery, New York, NY, USA, 771--780. https://doi.org/10.1145/1772690.1772769

Digital Library

[25]

Jordan Ramsdell and Laura Dietz. 2020. A Large Test Collection for Entity Aspect Linking. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (Virtual Event, Ireland) (CIKM '20). Association for Computing Machinery, New York, NY, USA, 3109--3116. https://doi.org/10.1145/3340531.3412875

Digital Library

[26]

Hadas Raviv, David Carmel, and Oren Kurland. 2012. A Ranking Framework for Entity Oriented Search Using Markov Random Fields. In Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search (Portland, Oregon, USA) (JIWES '12). Association for Computing Machinery, New York, NY, USA, Article 1, 6 pages. https://doi.org/10.1145/2379307.2379308

Digital Library

[27]

Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.

[28]

Michael Schuhmacher, Laura Dietz, and Simone Paolo Ponzetto. 2015. Ranking Entities for Web Queries Through Text and Knowledge. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (Melbourne, Australia) (CIKM '15). Association for Computing Machinery, New York, NY, USA, 1461--1470. https://doi.org/10.1145/2806416.2806480

Digital Library

[29]

Alberto Tonon, Gianluca Demartini, and Philippe Cudré-Mauroux. 2012. Combining Inverted Indices and Structured Search for Ad-Hoc Object Retrieval. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (Portland, Oregon, USA) (SIGIR '12). Association for Computing Machinery, New York, NY, USA, 125--134. https://doi.org/10.1145/2348283.2348304

Digital Library

[30]

Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji. 2018. Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia. CoRR, Vol. abs/1812.06280 (2018). arxiv: 1812.06280 http://arxiv.org/abs/1812.06280

[31]

Nikita Zhiltsov, Alexander Kotov, and Fedor Nikolaev. 2015. 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 (Santiago, Chile) (SIGIR '15). Association for Computing Machinery, New York, NY, USA, 253--262. https://doi.org/10.1145/2766462.2767756

Digital Library

Cited By

Jafarzadeh PAmirmahani ZEnsan F(2024)Learning contextual representations for entity retrievalApplied Intelligence10.1007/s10489-024-05430-054:19(8820-8840)Online publication date: 4-Jul-2024
https://doi.org/10.1007/s10489-024-05430-0
Mao TXu YLiu WPeng JChen LZhou M(2024)A simple but effective span-level tagging method for discontinuous named entity recognitionNeural Computing and Applications10.1007/s00521-024-09454-y36:13(7187-7201)Online publication date: 17-Feb-2024
https://dl.acm.org/doi/10.1007/s00521-024-09454-y
Chatterjee SMackie IDalton J(2024)DREQ: Document Re-ranking Using Entity-Based Query UnderstandingAdvances in Information Retrieval10.1007/978-3-031-56027-9_13(210-229)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56027-9_13
Show More Cited By

Index Terms

Entity Retrieval Using Fine-Grained Entity Aspects
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

Recommendations

Exploiting Entity Linking in Queries for Entity Retrieval
ICTIR '16: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval

The premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the ...
Mining, Ranking and Recommending Entity Aspects
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Entity queries constitute a large fraction of web search queries and most of these queries are in the form of an entity mention plus some context terms that represent an intent in the context of that entity. We refer to these entity-oriented search ...
Fine-grained Dutch named entity recognition

This paper describes the creation of a fine-grained named entity annotation scheme and corpus for Dutch, and experiments on automatic main type and subtype named entity recognition. We give an overview of existing named entity annotation schemes, and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 ACM.

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].

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

National Science Foundation

Conference

SIGIR '21

Sponsor:

SIGIR

SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
236
Total Downloads

Downloads (Last 12 months)35
Downloads (Last 6 weeks)5

Reflects downloads up to 12 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jafarzadeh PAmirmahani ZEnsan F(2024)Learning contextual representations for entity retrievalApplied Intelligence10.1007/s10489-024-05430-054:19(8820-8840)Online publication date: 4-Jul-2024
https://doi.org/10.1007/s10489-024-05430-0
Mao TXu YLiu WPeng JChen LZhou M(2024)A simple but effective span-level tagging method for discontinuous named entity recognitionNeural Computing and Applications10.1007/s00521-024-09454-y36:13(7187-7201)Online publication date: 17-Feb-2024
https://dl.acm.org/doi/10.1007/s00521-024-09454-y
Chatterjee SMackie IDalton J(2024)DREQ: Document Re-ranking Using Entity-Based Query UnderstandingAdvances in Information Retrieval10.1007/978-3-031-56027-9_13(210-229)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56027-9_13
Chatterjee S(2023)Answering Topical Information Needs Using Neural Entity-Oriented Information Retrieval and ExtractionACM SIGIR Forum10.1145/3582900.358292656:2(1-2)Online publication date: 31-Jan-2023
https://dl.acm.org/doi/10.1145/3582900.3582926
Lo PLim E(2023)A transformer framework for generating context-aware knowledge graph pathsApplied Intelligence10.1007/s10489-023-04588-353:20(23740-23767)Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1007/s10489-023-04588-3
Chatterjee SDietz LAl Hasan MXiong L(2022)Predicting Guiding Entities for Entity Aspect LinkingProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557671(3848-3852)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557671
Chatterjee SDietz LAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)BERT-ERProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531944(1466-1477)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531944
Jafarzadeh PAmirmahani ZEnsan FAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Learning to Rank Knowledge Subgraph Nodes for Entity RetrievalProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531888(2519-2523)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531888
Dietz LChatterjee SLennox CKashyapi SOza PGamari BAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)WikimarksProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531731(3003-3012)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531731
Mackie IOwoicho PGemmell CFischer SMacAvaney SDalton JAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)CODEC: Complex Document and Entity CollectionProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531712(3067-3077)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531712
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents