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Adaptive Knowledge Propagation in Web Ontologies

Authors:

Pasquale Minervini,

Claudia D'amato,

Nicola FanizziAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 12, Issue 1

Article No.: 2, Pages 1 - 28

https://doi.org/10.1145/3105961

Published: 21 August 2017 Publication History

Abstract

We focus on the problem of predicting missing assertions in Web ontologies. We start from the assumption that individual resources that are similar in some aspects are more likely to be linked by specific relations: this phenomenon is also referred to as homophily and emerges in a variety of relational domains. In this article, we propose a method for (1) identifying which relations in the ontology are more likely to link similar individuals and (2) efficiently propagating knowledge across chains of similar individuals. By enforcing sparsity in the model parameters, the proposed method is able to select only the most relevant relations for a given prediction task. Our experimental evaluation demonstrates the effectiveness of the proposed method in comparison to state-of-the-art methods from the literature.

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Cited By

Boland KFafalios PTchechmedjiev ADietze STodorov K(2022)Beyond facts – a survey and conceptualisation of claims in online discourse analysisSemantic Web10.3233/SW-21283813:5(793-827)Online publication date: 18-Aug-2022
https://doi.org/10.3233/SW-212838
d’Amato C(2021)Mining the Semantic Web with Machine Learning: Main Issues that Need to Be KnownReasoning Web. Declarative Artificial Intelligence 10.1007/978-3-030-95481-9_4(76-93)Online publication date: 8-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-95481-9_4
d’Amato C(2020)Machine Learning for the Semantic WebSemantic Web10.3233/SW-20038811:1(195-203)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-200388
Show More Cited By

Index Terms

Adaptive Knowledge Propagation in Web Ontologies
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Reasoning about belief and knowledge
      2. Semantic networks
  2. Machine learning
    1. Learning settings
      1. Semi-supervised learning settings
    2. Machine learning approaches
      1. Kernel methods
        Gaussian processes
      2. Logical and relational learning
        Statistical relational learning

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 12, Issue 1

February 2018

169 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3133955

Editors:
Brian D. Davison
Lehigh University, USA
,
Marianne Winslett
University of Illinois at Urbana-Champaign

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Copyright © 2017 ACM.

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Publication History

Published: 21 August 2017

Accepted: 01 May 2017

Revised: 01 May 2017

Received: 01 September 2014

Published in TWEB Volume 12, Issue 1

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Cited By

Boland KFafalios PTchechmedjiev ADietze STodorov K(2022)Beyond facts – a survey and conceptualisation of claims in online discourse analysisSemantic Web10.3233/SW-21283813:5(793-827)Online publication date: 18-Aug-2022
https://doi.org/10.3233/SW-212838
d’Amato C(2021)Mining the Semantic Web with Machine Learning: Main Issues that Need to Be KnownReasoning Web. Declarative Artificial Intelligence 10.1007/978-3-030-95481-9_4(76-93)Online publication date: 8-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-95481-9_4
d’Amato C(2020)Machine Learning for the Semantic WebSemantic Web10.3233/SW-20038811:1(195-203)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-200388
Shi MTang YZhu XLiu J(2020)Topic-aware Web Service Representation LearningACM Transactions on the Web10.1145/338604114:2(1-23)Online publication date: 11-Apr-2020
https://dl.acm.org/doi/10.1145/3386041
Sun XChen SFeng ZGe WHuang K(2018)A Service Annotation Quality Improvement Approach Based on Efficient Human Intervention2018 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2018.00021(107-114)Online publication date: Jul-2018
https://doi.org/10.1109/ICWS.2018.00021
Qazi AGoudar R(2018)An Ontology-based Term Weighting Technique for Web Document CategorizationProcedia Computer Science10.1016/j.procs.2018.07.010133(75-81)Online publication date: 2018
https://doi.org/10.1016/j.procs.2018.07.010

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