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Learning to discover complex mappings from web forms to ontologies

Authors:

Il-Yeol SongAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 1253 - 1262

https://doi.org/10.1145/2396761.2398427

Published: 29 October 2012 Publication History

Abstract

In order to realize the Semantic Web, various structures on the Web including Web forms need to be annotated with and mapped to domain ontologies. We present a machine learning-based automatic approach for discovering complex mappings from Web forms to ontologies. A complex mapping associates a set of semantically related elements on a form to a set of semantically related elements in an ontology. Existing schema mapping solutions mainly rely on integrity constraints to infer complex schema mappings. However, it is difficult to extract rich integrity constraints from forms. We show how machine learning techniques can be used to automatically discover complex mappings between Web forms and ontologies. The challenge is how to capture and learn the complicated knowledge encoded in existing complex mappings. We develop an initial solution that takes a naive Bayesian approach. We evaluated the performance of the solution on various domains. Our experimental results show that the solution returns the expected mappings as the top-1 results usually among several hundreds candidate mappings for more than 80% of the test cases. Furthermore, the expected mappings are always returned as the top-k results with k<4. The experiments have demonstrated that the approach is effective and has the potential to save significant human efforts.

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Cited By

Belgacem HLi XBianculli DBriand L(2022)A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry FormsACM Transactions on Software Engineering and Methodology10.1145/353302132:2(1-40)Online publication date: 24-May-2022
https://dl.acm.org/doi/10.1145/3533021
Thiéblin EHaemmerlé OTrojahn C(2021)Automatic evaluation of complex alignmentsSemantic Web10.3233/SW-21043712:5(767-787)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/SW-210437
Thiéblin EHaemmerlé OHernandez NTrojahn C(2020)Survey on complex ontology matchingSemantic Web10.3233/SW-19036611:4(689-727)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-190366
Show More Cited By

Index Terms

Learning to discover complex mappings from web forms to ontologies
1. Information systems
  1. Data management systems
    1. Information integration
      1. Extraction, transformation and loading
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Interoperability

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Published In

cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

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Publication History

Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Belgacem HLi XBianculli DBriand L(2022)A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry FormsACM Transactions on Software Engineering and Methodology10.1145/353302132:2(1-40)Online publication date: 24-May-2022
https://dl.acm.org/doi/10.1145/3533021
Thiéblin EHaemmerlé OTrojahn C(2021)Automatic evaluation of complex alignmentsSemantic Web10.3233/SW-21043712:5(767-787)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/SW-210437
Thiéblin EHaemmerlé OHernandez NTrojahn C(2020)Survey on complex ontology matchingSemantic Web10.3233/SW-19036611:4(689-727)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-190366
Chen YZhang XZhao QAkhtar FYang THuang KLi JWang Q(2020)An Ontology Based Approach for User Preference StatisticsFrontier Computing10.1007/978-981-15-3250-4_42(352-361)Online publication date: 26-Feb-2020
https://doi.org/10.1007/978-981-15-3250-4_42

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