research-article

Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries

Authors:

Alexander Kotov,

ChengXiang ZhaiAuthors Info & Claims

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

Pages 403 - 412

https://doi.org/10.1145/2124295.2124344

Published: 08 February 2012 Publication History

Abstract

Query expansion is an important and commonly used technique for improving Web search results. Existing methods for query expansion have mostly relied on global or local analysis of document collection, click-through data, or simple ontologies such as WordNet. In this paper, we present the results of a systematic study of the methods leveraging the ConceptNet knowledge base, an emerging new Web resource, for query expansion. Specifically, we focus on the methods leveraging ConceptNet to improve the search results for poorly performing (or difficult) queries. Unlike other lexico-semantic resources, such as WordNet and Wikipedia, which have been extensively studied in the past, ConceptNet features a graph-based representation model of commonsense knowledge, in which the terms are conceptually related through rich relational ontology. Such representation structure enables complex, multi-step inferences between the concepts, which can be applied to query expansion. We first demonstrate through simulation experiments that expanding queries with the related concepts from ConceptNet has great potential for improving the search results for difficult queries. We then propose and study several supervised and unsupervised methods for selecting the concepts from ConceptNet for automatic query expansion. The experimental results on multiple data sets indicate that the proposed methods can effectively leverage ConceptNet to improve the retrieval performance of difficult queries both when used in isolation as well as in combination with pseudo-relevance feedback.

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Cited By

Sheetrit ERaiber FKurland OYoshioka MKiseleva JAliannejadi M(2023)Entity-Based Relevance Feedback for Document RetrievalProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605128(177-187)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3578337.3605128
Pan MPei QLiu YLi THuang EWang JHuang J(2023)SPRF: A semantic Pseudo-relevance Feedback enhancement for information retrieval via ConceptNetKnowledge-Based Systems10.1016/j.knosys.2023.110602274(110602)Online publication date: Aug-2023
https://doi.org/10.1016/j.knosys.2023.110602
Cope BKalantzis MZhai CKrussel ASearsmith DFerguson DTapping RBerrocal Y(2022)Maps of Medical Reason: Applying Knowledge Graphs and Artificial Intelligence in Medical Education and PracticeBioinformational Philosophy and Postdigital Knowledge Ecologies10.1007/978-3-030-95006-4_8(133-159)Online publication date: 22-Apr-2022
https://doi.org/10.1007/978-3-030-95006-4_8
Show More Cited By

Index Terms

Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Information retrieval query processing

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cover image ACM Conferences

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

February 2012

792 pages

ISBN:9781450307475

DOI:10.1145/2124295

General Chairs:
Eytan Adar
University of Michigan, USA
,
Jaime Teevan
Microsoft Research, USA
,
Program Chairs:
Eugene Agichtein
Emory University, USA
,
Yoelle Maarek
Yahoo! Research, Israel

Copyright © 2012 ACM.

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Published: 08 February 2012

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WSDM'12: Fifth ACM International Conference on Web Search and Data Mining

February 8 - 12, 2012

Washington, Seattle, USA

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Cited By

Sheetrit ERaiber FKurland OYoshioka MKiseleva JAliannejadi M(2023)Entity-Based Relevance Feedback for Document RetrievalProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605128(177-187)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3578337.3605128
Pan MPei QLiu YLi THuang EWang JHuang J(2023)SPRF: A semantic Pseudo-relevance Feedback enhancement for information retrieval via ConceptNetKnowledge-Based Systems10.1016/j.knosys.2023.110602274(110602)Online publication date: Aug-2023
https://doi.org/10.1016/j.knosys.2023.110602
Cope BKalantzis MZhai CKrussel ASearsmith DFerguson DTapping RBerrocal Y(2022)Maps of Medical Reason: Applying Knowledge Graphs and Artificial Intelligence in Medical Education and PracticeBioinformational Philosophy and Postdigital Knowledge Ecologies10.1007/978-3-030-95006-4_8(133-159)Online publication date: 22-Apr-2022
https://doi.org/10.1007/978-3-030-95006-4_8
Zhang J(2021)Non-linear Relationship Learning Algorithm for Related Recommendations2021 International Conference on Big Data and Intelligent Decision Making (BDIDM)10.1109/BDIDM53834.2021.00019(58-64)Online publication date: Jul-2021
https://doi.org/10.1109/BDIDM53834.2021.00019
Bendella MQuafafou M(2021)Leveraging Closed Patterns and Formal Concept Analysis for Enhanced Microblogs RetrievalComplex Data Analytics with Formal Concept Analysis10.1007/978-3-030-93278-7_7(151-166)Online publication date: 8-Dec-2021
https://doi.org/10.1007/978-3-030-93278-7_7
Wu QFu DShen BChen Y(2020)Semantic Service Search in IT Crowdsourcing Platform: A Knowledge Graph-Based ApproachInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402040006930:06(765-783)Online publication date: 29-Jul-2020
https://doi.org/10.1142/S0218194020400069
Sharma DPamula RChauhan D(2020)A contemporary combined approach for query expansionMultimedia Tools and Applications10.1007/s11042-020-09172-281:24(35195-35221)Online publication date: 3-Jul-2020
https://doi.org/10.1007/s11042-020-09172-2
Mpouli SBeigbeder MLargeron C(2020)Lexifield: a system for the automatic building of lexicons by semantic expansion of short word listsKnowledge and Information Systems10.1007/s10115-020-01451-6Online publication date: 20-Mar-2020
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Abu Rasheed HWeber CZenkert JCzerner PKrumm RFathi M(2020)A Text Extraction-Based Smart Knowledge Graph Composition for Integrating Lessons Learned During the Microchip DesignIntelligent Systems and Applications10.1007/978-3-030-55187-2_43(594-610)Online publication date: 25-Aug-2020
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Chen TSubramanian SKrishnamurthy V(2019)Mini-Map: Mixed-Initiative Mind-Mapping via Contextual Query ExpansionAIAA Scitech 2019 Forum10.2514/6.2019-2347Online publication date: 6-Jan-2019
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