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Article

Web data extraction based on partial tree alignment

Authors:

Bing LiuAuthors Info & Claims

WWW '05: Proceedings of the 14th international conference on World Wide Web

Pages 76 - 85

https://doi.org/10.1145/1060745.1060761

Published: 10 May 2005 Publication History

Abstract

This paper studies the problem of extracting data from a Web page that contains several structured data records. The objective is to segment these data records, extract data items/fields from them and put the data in a database table. This problem has been studied by several researchers. However, existing methods still have some serious limitations. The first class of methods is based on machine learning, which requires human labeling of many examples from each Web site that one is interested in extracting data from. The process is time consuming due to the large number of sites and pages on the Web. The second class of algorithms is based on automatic pattern discovery. These methods are either inaccurate or make many assumptions. This paper proposes a new method to perform the task automatically. It consists of two steps, (1) identifying individual data records in a page, and (2) aligning and extracting data items from the identified data records. For step 1, we propose a method based on visual information to segment data records, which is more accurate than existing methods. For step 2, we propose a novel partial alignment technique based on tree matching. Partial alignment means that we align only those data fields in a pair of data records that can be aligned (or matched) with certainty, and make no commitment on the rest of the data fields. This approach enables very accurate alignment of multiple data records. Experimental results using a large number of Web pages from diverse domains show that the proposed two-step technique is able to segment data records, align and extract data from them very accurately.

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Cited By

Sarkhel RHuang BLockard CShiralkar P(2023)Self-Training for Label-Efficient Information Extraction from Semi-Structured Web-PagesProceedings of the VLDB Endowment10.14778/3611479.361151116:11(3098-3110)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611511
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
Prakash YSunkara MLee HJayarathna SAshok V(2023)AutoDesc: Facilitating Convenient Perusal of Web Data Items for Blind UsersProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584049(32-45)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584049
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Index Terms

Web data extraction based on partial tree alignment
1. Information systems
  1. Information retrieval
  2. Information storage systems

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Published In

cover image ACM Conferences

WWW '05: Proceedings of the 14th international conference on World Wide Web

May 2005

781 pages

ISBN:1595930469

DOI:10.1145/1060745

Conference Chairs:
Allan Ellis
Southern Cross University
,
Tatsuya Hagino
Keio University

Copyright © 2005 ACM.

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Published: 10 May 2005

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Cited By

Sarkhel RHuang BLockard CShiralkar P(2023)Self-Training for Label-Efficient Information Extraction from Semi-Structured Web-PagesProceedings of the VLDB Endowment10.14778/3611479.361151116:11(3098-3110)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611511
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
Prakash YSunkara MLee HJayarathna SAshok V(2023)AutoDesc: Facilitating Convenient Perusal of Web Data Items for Blind UsersProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584049(32-45)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584049
Chiou PAlotaibi AHalfond W(2023)BAGEL: An Approach to Automatically Detect Navigation-Based Web Accessibility Barriers for Keyboard UsersProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580749(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580749
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
Lee HAshok V(2022)Customizable Tabular Access to Web Data Records for Convenient Low-vision Screen Magnifier InteractionACM Transactions on Accessible Computing10.1145/351704415:2(1-22)Online publication date: 19-May-2022
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Jiménez PCorchuelo R(2022)On validating web information extraction proposalsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116700199:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116700
Aggarwal CAggarwal C(2022)Text Preparation and Similarity ComputationMachine Learning for Text10.1007/978-3-030-96623-2_2(19-32)Online publication date: 10-Feb-2022
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Cetorelli VAtzeni PCrescenzi VMilicchio F(2021)The smallest extraction problemProceedings of the VLDB Endowment10.14778/3476249.347629314:11(2445-2458)Online publication date: 27-Oct-2021
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Sun KWang FChen MPujara JDemartini GZuccon GCulpepper JHuang ZTong H(2021)Tabular Functional Block Detection with Embedding-based Agglomerative Cell ClusteringProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482484(1744-1753)Online publication date: 26-Oct-2021
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