Article

IEPAD: information extraction based on pattern discovery

Authors:

Chia-Hui Chang,

Shao-Chen LuiAuthors Info & Claims

WWW '01: Proceedings of the 10th international conference on World Wide Web

Pages 681 - 688

https://doi.org/10.1145/371920.372182

Published: 01 April 2001 Publication History

Get Access

References

[1]

Chang, C.H.; Lui, S.C.; and Wu, Y.C. Applying pattern mining to Web information extraction. In Proceedings of the Fifth Pacific Asia Conference on Knowledge Discovery and Data Mining, Apr. 2001, Hong Kong.]]

Digital Library

Google Scholar

[2]

Chien, L.F. PAT-tree-based keyword extraction for Chinese information retrieval. In Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval. pp. 50-58. 1997.]]

Digital Library

Google Scholar

[3]

Doorenbos, R.B.; Etzioni, O.; and Weld, D. S. A scalable comparison-shopping agent for the World Wide Web. In Proceedings of the first international conference on Autonomous Agents. pp. 39-48, NewYork, NY, 1997, ACM Press.]]

Digital Library

Google Scholar

[4]

Embley, D.; Jiang, Y.; and Ng, Y. -K. 1999. Recordboundary discovery in Web documents. In Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data (SIGMOD'99)}. pp. 467-478, Philadelphia, Pennsylvania.]]

Digital Library

Google Scholar

[5]

Gonnet, G.H.; Baeza-yates, R.A.; and Snider, T. 1992. New Indices for Text: Pat trees and Pat Arrays. Information Retrieval: Data Structures and Algorithms, Prentice Hall.]]

Digital Library

Google Scholar

[6]

Gusfield, D. 1997. Algorithms on strings, tree, and sequence, Cambridge. 1997.]]

Digital Library

Google Scholar

[7]

Hsu, C.-N., and Dung, M.-T. 1998. Generating finite-state transducers for semi-structured data extraction from the Web. Information Systems. 23(8): 521-538.]]

Digital Library

Google Scholar

[8]

Knoblock, A. et al., Eds. 1998. In Proceedings of the 1998 Workshop on AI and Information Integration, Menlo Park, California. AAAI Press.]]

Google Scholar

[9]

Kurtz, S., and Schleiermacher, C. 1999. REPuter: fast computation of maximal repeats in complete genomes. Bioinformatics 15(5): 426-427.]]

Crossref

Google Scholar

[10]

Kushmerick, N. 1999. Gleaning the Web. IEEE Intelligent Systems 14(2): 20-22.]]

Digital Library

Google Scholar

[11]

Kushmerick, N.; Weld, D.; and Doorenbos, R. 1997. Wrapper induction for information extraction. In Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI).]]

Google Scholar

[12]

Morrison, D. R. Journal of ACM, 15, pp. 514-534, 1968.]]

Digital Library

Google Scholar

[13]

Muslea, I.; Minton, S.; and Knoblock, C. 1999. A hierarchical approach to wrapper induction. In Proceedings of the 3rd International Conference on Autonomous Agents (Agents '99), Seattle, WA.]]

Digital Library

Google Scholar

[14]

Muslea, I. 1999. Extraction patterns for information extraction tasks: a survey. In Proceedings of AAAI '99: Workshop on Machine Learning for Information Extraction]]

Google Scholar

[15]

Sedgewick, R. Algorithms in C, Addison Wesley, 1990.]]

Digital Library

Google Scholar

Cited By

View all

Nunes MDorneles C(2023)EDREW - Enhanced Data Representation for Extraction in WebProceedings of the 29th Brazilian Symposium on Multimedia and the Web10.1145/3617023.3617055(230-237)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617023.3617055
Chen ZMeng WDragut E(2022)Web Record Extraction with InvariantsProceedings of the VLDB Endowment10.14778/3574245.357427616:4(959-972)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.14778/3574245.3574276
Kumaresan URamanujam K(2021)A Framework for Automated Scraping of Structured Data Records From the Deep Web Using Semantic LabelingInternational Journal of Information Retrieval Research10.4018/IJIRR.29083012:1(1-18)Online publication date: 4-Nov-2021
https://doi.org/10.4018/IJIRR.290830
Show More Cited By

Index Terms

IEPAD: information extraction based on pattern discovery

Recommendations

Automatic information extraction from semi-structured Web pages by pattern discovery
Web retrieval and mining

The World Wide Web is now undeniably the richest and most dense source of information; yet, its structure makes it difficult to make use of that information in a systematic way. This paper proposes a pattern discovery approach to the rapid generation of ...
Heuristic learning of rules for information extraction from web documents
InfoScale '07: Proceedings of the 2nd international conference on Scalable information systems

The efficacy of an information extraction system is mostly determined by the quality of the extraction rules. Building these extraction rules is time-consuming and difficult to implement by hand. Hence, we propose a Heuristic Rule Learning (HRL) ...
OLERA: Semisupervised Web-Data Extraction with Visual Support

Extracting information from semistructured Web documents is an important task for many information agents. Over the past few years, researchers have developed an extensive family of generic information extraction techniques based on supervised ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

WWW '01: Proceedings of the 10th international conference on World Wide Web

May 2001

770 pages

ISBN:1581133480

DOI:10.1145/371920

Chairmen:
Vincent Y. Shen
Hong Kong Univ. of Science and Technology
,
Nobuo Saito
Keio Univ., Japan
,
Michael R. Lyu
Chinese Univ. of Hong Kong, HK
,
Mary Ellen Zurko
Iris Associates,USA

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]

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2001

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

WWW01

Sponsor:

IW3C2

WWW01: Hypermedia Track of the 10th International World Wide Web Conference

May 1 - 5, 2001

Hong Kong, Hong Kong

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

306
Total Citations
View Citations
2,435
Total Downloads

Downloads (Last 12 months)25
Downloads (Last 6 weeks)1

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Nunes MDorneles C(2023)EDREW - Enhanced Data Representation for Extraction in WebProceedings of the 29th Brazilian Symposium on Multimedia and the Web10.1145/3617023.3617055(230-237)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617023.3617055
Chen ZMeng WDragut E(2022)Web Record Extraction with InvariantsProceedings of the VLDB Endowment10.14778/3574245.357427616:4(959-972)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.14778/3574245.3574276
Kumaresan URamanujam K(2021)A Framework for Automated Scraping of Structured Data Records From the Deep Web Using Semantic LabelingInternational Journal of Information Retrieval Research10.4018/IJIRR.29083012:1(1-18)Online publication date: 4-Nov-2021
https://doi.org/10.4018/IJIRR.290830
Patnaik SNarendra Babu C(2021)Trends in web data extraction using machine learningWeb Intelligence10.3233/WEB-210465(1-22)Online publication date: 15-Nov-2021
https://doi.org/10.3233/WEB-210465
Chen QLamoreaux AWang XDurrett GBastani ODillig IFreund SYahav E(2021)Web question answering with neurosymbolic program synthesisProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454047(328-343)Online publication date: 19-Jun-2021
https://dl.acm.org/doi/10.1145/3453483.3454047
Kumar SKumar R(2021)A Study on Different Aspects of Web Mining and Research IssuesIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/1022/1/0120181022(012018)Online publication date: 19-Jan-2021
https://doi.org/10.1088/1757-899X/1022/1/012018
Tekli G(2021)A survey on semi-structured web data manipulations by non-expert usersComputer Science Review10.1016/j.cosrev.2021.10036740(100367)Online publication date: May-2021
https://doi.org/10.1016/j.cosrev.2021.100367
Merkaš ZPerkov DBonin V(2020)The Significance of Blockchain Technology in Digital Transformation of Logistics and TransportationInternational Journal of E-Services and Mobile Applications10.4018/IJESMA.202001010112:1(1-20)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.4018/IJESMA.2020010101
Aakash AJaiswal A(2020)Segmentation and Ranking of Online Reviewer CommunityInternational Journal of E-Adoption10.4018/IJEA.202001010612:1(63-83)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.4018/IJEA.2020010106
Goyal VBansal NRani S(2020)Experimenting Language Identification for Sentiment Analysis of English Punjabi Code Mixed Social Media TextInternational Journal of E-Adoption10.4018/IJEA.202001010512:1(52-62)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.4018/IJEA.2020010105
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.

Cited By

Index Terms

Recommendations

Automatic information extraction from semi-structured Web pages by pattern discovery

Heuristic learning of rules for information extraction from web documents

OLERA: Semisupervised Web-Data Extraction with Visual Support