short-paper

Reducing the search space in ontology alignment using clustering techniques and topic identification

Authors:

Agnese Chiatti,

Zlatan Dragisic,

Tania Cerquitelli,

Patrick LambrixAuthors Info & Claims

K-CAP '15: Proceedings of the 8th International Conference on Knowledge Capture

Article No.: 21, Pages 1 - 4

https://doi.org/10.1145/2815833.2816959

Published: 07 October 2015 Publication History

Abstract

One of the current challenges in ontology alignment is scalability and one technique to deal with this issue is to reduce the search space for the generation of mapping suggestions. In this paper we develop a method to prune that search space by using clustering techniques and topic identification. Further, we provide experiments showing that we are able to generate partitions that allow for high quality alignments with a highly reduced effort for computation and validation of mapping suggestions for the parts of the ontologies in the partition. Other techniques will still be needed for finding mappings that are not in the partition.

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Cited By

Laadhar AGhozzi FMegdiche IRavat FTeste OGargouri FHung CPapadopoulos G(2019)Partitioning and local matching learning of large biomedical ontologiesProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297507(2285-2292)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297507
Laadhar AGhozzi FMegdiche IRavat FTeste OGargouri F(2019)The Impact of Imbalanced Training Data on Local Matching Learning of OntologiesBusiness Information Systems10.1007/978-3-030-20485-3_13(162-175)Online publication date: 18-May-2019
https://doi.org/10.1007/978-3-030-20485-3_13
Tiwari AKumar A(2018)Comparative Analysis of Optimized Algorithms for Ontology Clustering2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)10.1109/UPCON.2018.8597150(1-7)Online publication date: Nov-2018
https://doi.org/10.1109/UPCON.2018.8597150
Show More Cited By

Index Terms

Reducing the search space in ontology alignment using clustering techniques and topic identification
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Sorting and searching
  2. Logic

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Published In

cover image ACM Other conferences

K-CAP '15: Proceedings of the 8th International Conference on Knowledge Capture

October 2015

209 pages

ISBN:9781450338493

DOI:10.1145/2815833

Conference Chair:
Ken Barker
IBM Watson Research, USA
,
Program Chair:
José Manuel Gómez-Pérez
Expert System, Spain

Copyright © 2015 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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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 October 2015

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Short-paper
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Refereed limited

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K-CAP 2015

K-CAP 2015: Knowledge Capture Conference

October 7 - 10, 2015

NY, Palisades, USA

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K-CAP '15 Paper Acceptance Rate 16 of 56 submissions, 29%;

Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Laadhar AGhozzi FMegdiche IRavat FTeste OGargouri FHung CPapadopoulos G(2019)Partitioning and local matching learning of large biomedical ontologiesProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297507(2285-2292)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297507
Laadhar AGhozzi FMegdiche IRavat FTeste OGargouri F(2019)The Impact of Imbalanced Training Data on Local Matching Learning of OntologiesBusiness Information Systems10.1007/978-3-030-20485-3_13(162-175)Online publication date: 18-May-2019
https://doi.org/10.1007/978-3-030-20485-3_13
Tiwari AKumar A(2018)Comparative Analysis of Optimized Algorithms for Ontology Clustering2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)10.1109/UPCON.2018.8597150(1-7)Online publication date: Nov-2018
https://doi.org/10.1109/UPCON.2018.8597150
Lambrix PKaliyaperumal RCheatham MCruz IEuzenat JPesquita C(2017)A session-based ontology alignment approach enabling user involvementSemantic Web10.3233/SW-1602438:2(225-251)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/SW-160243
Cerquitelli TDi Corso EVentura FChiusano SAkerkar RCuzzocrea ACao JHacid M(2017)Data miners' little helperProceedings of the 7th International Conference on Web Intelligence, Mining and Semantics10.1145/3102254.3102288(1-6)Online publication date: 19-Jun-2017
https://dl.acm.org/doi/10.1145/3102254.3102288

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