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Combining URL and HTML Features for Entity Discovery in the Web

Authors:

Carina Friedrich Dorneles, and

Renata GalanteAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 13, Issue 4

Article No.: 20, Pages 1 - 27

https://doi.org/10.1145/3365574

Published: 04 December 2019 Publication History

Abstract

The web is a large repository of entity-pages. An entity-page is a page that publishes data representing an entity of a particular type, for example, a page that describes a driver on a website about a car racing championship. The attribute values published in the entity-pages can be used for many data-driven companies, such as insurers, retailers, and search engines. In this article, we define a novel method, called SSUP, which discovers the entity-pages on the websites. The novelty of our method is that it combines URL and HTML features in a way that allows the URL terms to have different weights depending on their capacity to distinguish entity-pages from other pages, and thus the efficacy of the entity-page discovery task is increased. SSUP determines the similarity thresholds on each website without human intervention. We carried out experiments on a dataset with different real-world websites and a wide range of entity types. SSUP achieved a 95% rate of precision and 85% recall rate. Our method was compared with two state-of-the-art methods and outperformed them with a precision gain between 51% and 66%.

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Cited By

Uzun E(2023)Scraping Relevant Images from Web Pages without DownloadACM Transactions on the Web10.1145/361684918:1(1-27)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3616849
Hegade PLingadhal NJain SKhan UVijeth K(2021)Crawler by Contextual InferenceSN Computer Science10.1007/s42979-021-00574-z2:3Online publication date: 16-Apr-2021
https://dl.acm.org/doi/10.1007/s42979-021-00574-z

Index Terms

Combining URL and HTML Features for Entity Discovery in the Web
1. Information systems
  1. Data management systems
    1. Information integration
      1. Wrappers (data mining)
  2. World Wide Web
    1. Web mining
      1. Site wrapping
    2. Web searching and information discovery
      1. Web search engines
        Web crawling

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Published In

ACM Transactions on the Web Volume 13, Issue 4

November 2019

139 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3372405

Editor:
Brian D. Davison
Lehigh University, USA

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Copyright © 2019 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 December 2019

Accepted: 01 September 2019

Revised: 01 February 2019

Received: 01 February 2017

Published in TWEB Volume 13, Issue 4

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Cited By

Uzun E(2023)Scraping Relevant Images from Web Pages without DownloadACM Transactions on the Web10.1145/361684918:1(1-27)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3616849
Hegade PLingadhal NJain SKhan UVijeth K(2021)Crawler by Contextual InferenceSN Computer Science10.1007/s42979-021-00574-z2:3Online publication date: 16-Apr-2021
https://dl.acm.org/doi/10.1007/s42979-021-00574-z

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