Article

Extracting semantic relations from query logs

Authors:

Ricardo Baeza-Yates,

Alessandro TiberiAuthors Info & Claims

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 76 - 85

https://doi.org/10.1145/1281192.1281204

Published: 12 August 2007 Publication History

Abstract

In this paper we study a large query log of more than twenty million queries with the goal of extracting the semantic relations that are implicitly captured in the actions of users submitting queries and clicking answers. Previous query log analyses were mostly done with just the queries and not the actions that followed after them. We first propose a novel way to represent queries in a vector space based on a graph derived from the query-click bipartite graph. We then analyze the graph produced by our query log, showing that it is less sparse than previous results suggested, and that almost all the measures of these graphs follow power laws, shedding some light on the searching user behavior as well as on the distribution of topics that people want in the Web. The representation we introduce allows to infer interesting semantic relationships between queries. Second, we provide an experimental analysis on the quality of these relations, showing that most of them are relevant. Finally we sketch an application that detects multitopical URLs.

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References

[1]

R. Baeza-Yates. Applications of web query mining. ECIR'05.

Digital Library

[2]

R. Baeza-Yates, C. Hurtado, and M. Mendoza. Query clustering for boosting web page ranking. AWIC'04,

[3]

R. Baeza-Yates, C. Hurtado, and M. Mendoza. Query recommendation using query logs in a search engine. EDBT Workshops, 2004.

Digital Library

[4]

D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. KDD'99. Boston, MA USA.

Digital Library

[5]

A. Cid, C- Hurtado, and M- Mendoza. Automatic maintenance of Web directories using clickthrough data. WIRI'06.

Digital Library

[6]

S.-L. Chuang and L.-F. Chien. Automatic query taxonomy generation for information retrieval applications. Online Information Review 27(4), 2003.

[7]

S.-L. Chuang and L.-F. Chien. Enriching web taxonomies through subject categorization of query terms from search engine logs. Decision Support System 30(1), 2003.

Digital Library

[8]

S.-L. Chuang and L.-F. Chien. Towards automatic generation of query taxonomy: A hierarchical query clustering approach. ICDM'02.

Digital Library

[9]

P.-J. Cheng, C.-H. Tsai, C.-M. Hung, and L.-F. Chien. Query Taxonomy Generation for Web Search (poster). CIKM'06.

Digital Library

[10]

G. Dupret and M. Mendoza. Automatic Query Recommendation using Click-Through Data. IFIP PPAI'06.

[11]

L. Fitzpatrick and M. Dent. Automatic feedback using past queries: Social searching? In SIGIR'97.

Digital Library

[12]

B. M. Fonseca, P. B Golgher, E. S. De Moura, and N. Ziviani. Using association rules to discovery search engines related queries. In LA-WEB'03.

Digital Library

[13]

H.-T. Pu, S.-L. Chuang, and C. Yang. Subject categorization of query terms for exploring web users' search interests. JASIST 53(8), 2002.

Digital Library

[14]

V. V. Raghavan and H. Sever. On the reuse of past optimal queries. SIGIR'95.

Digital Library

[15]

M. Sahami and T. D. Heilman. A web-based kernel function for measuring the similarity of short text snippets. WWW'06.

Digital Library

[16]

James Surowiecki. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, Little and Brown, 2004.

Digital Library

[17]

J. Wen, J. Mie, and H. Zhang. Clustering user queries of a search engine. WWW'01.

Digital Library

[18]

H.-J. Zeng, Q.-C. He, Z. Chen, W.-Y. Ma, and J. Ma. Learning To Cluster Search Results. SIGIR'04.

Digital Library

Cited By

You GDaum MStoll S(2024)Adults Adapt to Child Speech in Causative SemanticsCognitive Science10.1111/cogs.1349548:9Online publication date: 16-Sep-2024
https://doi.org/10.1111/cogs.13495
Palumbo EDamianou AWang ALiu AFazelnia GFabbri FFerreira RSilvestri FBouchard HHauff CLalmas MCarterette BChandar PNyhan DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Graph Learning for Exploratory Query Suggestions in an Instant Search SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615481(4780-4786)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615481
Li PGaurav (2023)Automatic Synonym Extraction and Context-based Query Reformulation for Points-of-Interest Search2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00235(3072-3078)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00235
Show More Cited By

Index Terms

Extracting semantic relations from query logs
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
  2. Information systems applications
    1. Data mining

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Information & Contributors

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Published In

cover image ACM Conferences

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2007

1080 pages

ISBN:9781595936097

DOI:10.1145/1281192

General Chair:
Pavel Berkhin
Yahoo!, USA
,
Program Chairs:
Rich Caruana
Cornell University, USA
,
Xindong Wu
University of Vermont, USA

Copyright © 2007 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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Publication History

Published: 12 August 2007

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KDD07

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KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 15, 2007

California, San Jose, USA

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KDD '07 Paper Acceptance Rate 111 of 573 submissions, 19%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

You GDaum MStoll S(2024)Adults Adapt to Child Speech in Causative SemanticsCognitive Science10.1111/cogs.1349548:9Online publication date: 16-Sep-2024
https://doi.org/10.1111/cogs.13495
Palumbo EDamianou AWang ALiu AFazelnia GFabbri FFerreira RSilvestri FBouchard HHauff CLalmas MCarterette BChandar PNyhan DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Graph Learning for Exploratory Query Suggestions in an Instant Search SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615481(4780-4786)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615481
Li PGaurav (2023)Automatic Synonym Extraction and Context-based Query Reformulation for Points-of-Interest Search2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00235(3072-3078)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00235
Barman DSarkar RChowdhury N(2023)A cooperative co-evolutionary genetic algorithm for query recommendationMultimedia Tools and Applications10.1007/s11042-023-15585-683:4(11461-11491)Online publication date: 29-Jun-2023
https://doi.org/10.1007/s11042-023-15585-6
Lillo FRuggieri S(2022)Estimating the Total Volume of Queries to a Search EngineIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.305466834:11(5351-5363)Online publication date: 1-Nov-2022
https://doi.org/10.1109/TKDE.2021.3054668
Chen HZhang CLi JYu PJing N(2022)KGGen: A Generative Approach for Incipient Knowledge Graph PopulationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.301416634:5(2254-2267)Online publication date: 1-May-2022
https://doi.org/10.1109/TKDE.2020.3014166
Korzeniowski LGoczyla K(2022)Landscape of Automated Log Analysis: A Systematic Literature Review and Mapping StudyIEEE Access10.1109/ACCESS.2022.315254910(21892-21913)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3152549
Deepak GSanthanavijayan A(2021)UQSCM-RFD: A query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interactionNeural Computing and Applications10.1007/s00521-021-06404-wOnline publication date: 21-Aug-2021
https://doi.org/10.1007/s00521-021-06404-w
Reddy RVaishnavi CMamatha CAnanthakumaran S(2021)Location‐Aware Keyword Query Suggestion Techniques With Artificial Intelligence PerspectiveComputational Analysis and Deep Learning for Medical Care10.1002/9781119785750.ch2(35-51)Online publication date: 13-Aug-2021
https://doi.org/10.1002/9781119785750.ch2
Craswell NCampos DMitra BYilmaz EBillerbeck Bd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing SearchProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412779(2983-2989)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412779
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