research-article

Structured annotations of web queries

Authors:

Stelios Paparizos,

Panayiotis TsaparasAuthors Info & Claims

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Pages 771 - 782

https://doi.org/10.1145/1807167.1807251

Published: 06 June 2010 Publication History

Abstract

Queries asked on web search engines often target structured data, such as commercial products, movie showtimes, or airline schedules. However, surfacing relevant results from such data is a highly challenging problem, due to the unstructured language of the web queries, and the imposing scalability and speed requirements of web search. In this paper, we discover latent structured semantics in web queries and produce Structured Annotations for them. We consider an annotation as a mapping of a query to a table of structured data and attributes of this table. Given a collection of structured tables, we present a fast and scalable tagging mechanism for obtaining all possible annotations of a query over these tables. However, we observe that for a given query only few are sensible for the user needs. We thus propose a principled probabilistic scoring mechanism, using a generative model, for assessing the likelihood of a structured annotation, and we define a dynamic threshold for filtering out misinterpreted query annotations. Our techniques are completely unsupervised, obviating the need for costly manual labeling effort. We evaluated our techniques using real world queries and data and present promising experimental results.

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Cited By

Stoilos GPapasarantopoulos NVougiouklis PBansky PZhang ARangwala H(2022)Type Linking for Query Understanding and Semantic SearchProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539067(3931-3940)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539067
Bassani EPasi G(2022)Evaluating the Use of Synthetic Queries for Pre-training a Semantic Query TaggerAdvances in Information Retrieval10.1007/978-3-030-99739-7_5(39-46)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_5
Bassani EPasi GDiaz FShah CSuel TCastells PJones RSakai T(2021)Semantic Query Labeling Through Synthetic Query GenerationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463071(2278-2282)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463071
Show More Cited By

Index Terms

Structured annotations of web queries
1. Information systems
  1. Information retrieval

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Information & Contributors

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Published In

cover image ACM Conferences

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

June 2010

1286 pages

ISBN:9781450300322

DOI:10.1145/1807167

General Chair:
Ahmed Elmagarmid
Purdue University, USA
,
Program Chair:
Divyakant Agrawal
University of California at Santa Barbara, USA

Copyright © 2010 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 ACM 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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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

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Published: 06 June 2010

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SIGMOD/PODS '10

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SIGMOD

SIGMOD/PODS '10: International Conference on Management of Data

June 6 - 10, 2010

Indiana, Indianapolis, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Stoilos GPapasarantopoulos NVougiouklis PBansky PZhang ARangwala H(2022)Type Linking for Query Understanding and Semantic SearchProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539067(3931-3940)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539067
Bassani EPasi G(2022)Evaluating the Use of Synthetic Queries for Pre-training a Semantic Query TaggerAdvances in Information Retrieval10.1007/978-3-030-99739-7_5(39-46)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_5
Bassani EPasi GDiaz FShah CSuel TCastells PJones RSakai T(2021)Semantic Query Labeling Through Synthetic Query GenerationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463071(2278-2282)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463071
Hirsch SGuy INus ADagan AKurland OHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Query Reformulation in E-Commerce SearchProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401065(1319-1328)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401065
Balog KBalog K(2018)Understanding Information NeedsEntity-Oriented Search10.1007/978-3-319-93935-3_7(225-267)Online publication date: 3-Oct-2018
https://doi.org/10.1007/978-3-319-93935-3_7
Balog KBalog K(2018)IntroductionEntity-Oriented Search10.1007/978-3-319-93935-3_1(1-23)Online publication date: 3-Oct-2018
https://doi.org/10.1007/978-3-319-93935-3_1
Geethanjali KSuresh KKanaka Raju R(2017)Cluster Based Prediction of Keyword Query Over DatabasesComputer Communication, Networking and Internet Security10.1007/978-981-10-3226-4_25(253-260)Online publication date: 4-May-2017
https://doi.org/10.1007/978-981-10-3226-4_25
Chiang FAndritsos PMiller R(2016)Data Driven Discovery of Attribute DictionariesTransactions on Computational Collective Intelligence XXI - Volume 963010.5555/3090176.3090180(69-96)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.5555/3090176.3090180
Kim YCollins-Thompson KTeevan J(2016)Using the Crowd to Improve Search Result Ranking and the Search ExperienceACM Transactions on Intelligent Systems and Technology10.1145/28973687:4(1-24)Online publication date: 12-Jul-2016
https://dl.acm.org/doi/10.1145/2897368
Chen CXing ZHan L(2016)TechLand: Assisting Technology Landscape Inquiries with Insights from Stack Overflow2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2016.17(356-366)Online publication date: Oct-2016
https://doi.org/10.1109/ICSME.2016.17
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