Article

A graph-theoretic approach to extract storylines from search results

Authors:

D. SivakumarAuthors Info & Claims

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 216 - 225

https://doi.org/10.1145/1014052.1014078

Published: 22 August 2004 Publication History

Abstract

We present a graph-theoretic approach to discover storylines from search results. Storylines are windows that offer glimpses into interesting themes latent among the top search results for a query; they are different from, and complementary to, clusters obtained through traditional approaches. Our framework is axiomatically developed and combinatorial in nature, based on generalizations of the maximum induced matching problem on bipartite graphs. The core algorithmic task involved is to mine for signature structures in a robust graph representation of the search results. We present a very fast algorithm for this task based on local search. Experiments show that the collection of storylines extracted through our algorithm offers a concise organization of the wealth of information hidden beyond the first page of search results.

References

[1]

K. Bharat and M. Henzinger. Improved algorithms for topic distillation in a hyperlinked environment. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 104--111, 1998.

Digital Library

[2]

S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. In Proceedings of the 7th International World Wide Web Conference, pages 107--117, 1998.

Digital Library

[3]

J-Y. Cai. On the impossibility of certain ranking functions. International Mathematical Journal, 3(2):119--128, 2003.

[4]

T. Calishain. Clustering with search engines. http://www.llrx.com/features/clusteringsearch.htm.

[5]

S. Chakrabarti, B. Dom, D. Gibson, R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Spectral filtering for resource discovery. In Proceedings of the ACM SIGIR Workshop on Hypertext Analysis, pages 13--21, 1998.

[6]

D. R. Cutting, D. R. Karger, J. O. Pedersen, and J. W. Tuckey. Scatter/gather: A cluster-based approach to browsing large document collections. In Proceedings of the 15th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 318--329, 1992.

Digital Library

[7]

S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Fumas, and R. A. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391--407, 1990.

[8]

I. Dhillon, S. Mallela, and D. Modha. Information-theoretic co-clustering. In Proc. ACM Conference on Knowledge Discovery and Data Mining, pages 89--98, 2003.

Digital Library

[9]

R. Kannan, S. Vempala, and A. Vetta. On clusterings: Good, bad, and spectral. In Proceedings of the 41st Symposium on Foundations of Computer Science, pages 367--377, 2000.

Digital Library

[10]

J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, 1999.

Digital Library

[11]

R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the web for emerging cyber-communities. WWW8/Computer Networks, 31(11--16):481--493, 1999.

Digital Library

[12]

L. Stockmeyer and V. Vazirani. NP-Completeness of some generalizations of the maximum matching problem. Information Processing Letters, 15(1):14--19, 1982.

[13]

N. Tishby, F.C. Pereira, and W. Blalek. The information bottleneck method. In Proc. 37th Annual Allerton Conference on Communication, Control, and Computing, pages 368--377, 1999.

[14]

P. Tsaparas. Link Analysis Ranking Algorithms. PhD thesis, University of Toronto, 2003.

Digital Library

[15]

O. Zamir and O. Etzioni. A dynamic clustering interface to web search results. WWW8/Computer Networks, 31(11--16):1361--1374, 1999.

Digital Library

[16]

O. Zamir, O. Etzioni, O. Madani, and R. M. Karp. Fast and intuitive clustering of web documents. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pages 287--290, 1997.

Cited By

Pang HQi XXiao CXu ZDing GChang YYang XDuan T(2024)Pottery evolution pattern discovery based on deep learning: case study of Miaozigou culture in ChinaHeritage Science10.1186/s40494-024-01468-y12:1Online publication date: 10-Oct-2024
https://doi.org/10.1186/s40494-024-01468-y
Habib MMouatadid L(2019)Maximum Induced Matching Algorithms via Vertex Ordering CharacterizationsAlgorithmica10.1007/s00453-018-00538-5Online publication date: 19-Jan-2019
https://doi.org/10.1007/s00453-018-00538-5
Samuel ASharma D(2018)Location estimation of non-geo-tagged tweetsEvolutionary Intelligence10.1007/s12065-018-0163-3Online publication date: 14-Aug-2018
https://doi.org/10.1007/s12065-018-0163-3
Show More Cited By

Index Terms

A graph-theoretic approach to extract storylines from search results
1. Information systems
  1. Information retrieval
  2. World Wide Web
    1. Web applications
    2. Web services
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

Recommendations

Constructing and Connecting Storylines to Tell Museum Stories
ICIDS 2013: Proceedings of the 6th International Conference on Interactive Storytelling - Volume 8230

Over the past decade a number of systems have been developed that tell museum stories by constructing digital presentations from cultural objects and their metadata. Our novel approach, informed by museum practice, is built around a formalization of ...
The Usefulness of Search Results: A Systematization of Types and Predictors
CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

In evaluating search systems there is a growing trend to complement the established effectiveness indicator topical relevance by the usefulness of search results. Usefulness refers to the contribution of search results to a larger task generating ...
Decision Graph Search

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2004

874 pages

ISBN:1581138881

DOI:10.1145/1014052

General Chairs:
Won Kim
Cyber Database Solutions
,
Ronny Kohavi
Amazon.com
,
Program Chairs:
Johannes Gehrke
Cornell University
,
William DuMouchel
AT&T Labs Research

Copyright © 2004 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 August 2004

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

KDD04

Sponsor:

KDD04: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 22 - 25, 2004

WA, Seattle, USA

Acceptance Rates

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

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

37
Total Citations
View Citations
1,584
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 20 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Pang HQi XXiao CXu ZDing GChang YYang XDuan T(2024)Pottery evolution pattern discovery based on deep learning: case study of Miaozigou culture in ChinaHeritage Science10.1186/s40494-024-01468-y12:1Online publication date: 10-Oct-2024
https://doi.org/10.1186/s40494-024-01468-y
Habib MMouatadid L(2019)Maximum Induced Matching Algorithms via Vertex Ordering CharacterizationsAlgorithmica10.1007/s00453-018-00538-5Online publication date: 19-Jan-2019
https://doi.org/10.1007/s00453-018-00538-5
Samuel ASharma D(2018)Location estimation of non-geo-tagged tweetsEvolutionary Intelligence10.1007/s12065-018-0163-3Online publication date: 14-Aug-2018
https://doi.org/10.1007/s12065-018-0163-3
Samuel ASharma DMishra K(2017)A spatial, temporal and sentiment based framework for indexing and clustering in twitter blogosphereJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-16929732:5(3619-3632)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/JIFS-169297
Samuel ASharma D(2017)Summary generation using geo-coordinates and temporal data in microblogging environment2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT.2017.8204049(1-5)Online publication date: Jul-2017
https://doi.org/10.1109/ICCCNT.2017.8204049
Zhou HYu HHu RHu J(2017)A survey on trends of cross-media topic evolution mapKnowledge-Based Systems10.1016/j.knosys.2017.03.009124:C(164-175)Online publication date: 15-May-2017
https://dl.acm.org/doi/10.1016/j.knosys.2017.03.009
Zhou HYu HHu R(2017)Topic evolution based on the probabilistic topic modelFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-5442-511:5(786-802)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s11704-016-5442-5
Tang SWu FLi SLu WZhang ZZhuang Y(2015)Sketch the storyline with CHARCOALProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832747.2832785(3841-3848)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832747.2832785
Qian SZhang TXu CHossain M(2015)Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet AllocationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/265952111:2(1-22)Online publication date: 7-Jan-2015
https://dl.acm.org/doi/10.1145/2659521
Zhang TXu C(2014)Cross-Domain Multi-Event Tracking via CO-PMHTACM Transactions on Multimedia Computing, Communications, and Applications10.1145/260263310:4(1-19)Online publication date: 4-Jul-2014
https://dl.acm.org/doi/10.1145/2602633
Show More Cited By

View Options

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.

Figures

Tables

Media

View Table of Conten