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LogMap-P: On matching ontologies in parallel

Published: 05 January 2018 Publication History

Abstract

An enormous amount of research have been published related to ontology matching. The core motivation behind these researches aim to develop matching techniques that result in highly accurate ontology matching systems. However the performance (in terms of execution time) of these matching techniques is predominantly unexplored and is equally important. Among the well established research implementations, LogMap an open source system, is considered as state-of-the-art in ontology matching due to its accuracy. This paper presents LogMap-P, an enhanced version of LogMap with motivation to boost performance while preserving the accuracy of the matching techniques.

References

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Muhammad Bilal Amin, Rabia Batool, Wajahat Ali Khan, Sungyoung Lee, and Eui-Nam Huh. 2014. SPHeRe. The Journal of Supercomputing 68, 1 (01 Apr 2014), 274--301.
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Muhammad Bilal Amin, Wajahat Ali Khan, Sungyoung Lee, and Byeong Ho Kang. 2015. Performance-based ontology matching. Applied Intelligence 43, 2 (01 Sep 2015), 356--385.
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Jrme Euzenat and Pavel Shvaiko. 2013. Ontology Matching (2nd ed.). Springer Publishing Company, Incorporated.
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Anika Gross, Michael Hartung, Toralf Kirsten, and Erhard Rahm. 2010. On Matching Large Life Science Ontologies in Parallel. Springer Berlin Heidelberg, Berlin, Heidelberg, 35--49.
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Ernesto Jiménez-Ruiz and Bernardo Cuenca Grau. 2011. Logmap: Logic-based and scalable ontology matching. In International Semantic Web Conference. Springer, 273--288.
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Wajahat Ali Khan, Muhammad Bilal Amin, Asad Masood Khattak, Maqbool Hussain, and Sungyoung Lee. 2013. System for Parallel Heterogeneity Resolution (SPHeRe) Results for OAEI 2013. In Proceedings of the 8th International Conference on Ontology Matching - Volume 1111 (OM'13). CEUR-WS.org, Aachen, Germany, Germany, 184--189. http://dl.acm.org/citation.cfm?id=2874493.2874511
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Toralf Kirsten, Anika Gross, Michael Hartung, and Erhard Rahm. 2011. GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution. Journal of Biomedical Semantics 2, 1 (13 Sep 2011), 6.
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P. Shvaiko and J. Euzenat. 2013. Ontology Matching: State of the Art and Future Challenges. IEEE Transactions on Knowledge and Data Engineering 25, 1 (Jan 2013), 158--176.

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Published In

cover image ACM Other conferences
IMCOM '18: Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication
January 2018
628 pages
ISBN:9781450363853
DOI:10.1145/3164541
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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  • SKKU: SUNGKYUNKWAN UNIVERSITY

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 January 2018

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Author Tags

  1. Ontology matching
  2. Parallel matching
  3. Semantic web

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IMCOM '18

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IMCOM '18 Paper Acceptance Rate 100 of 255 submissions, 39%;
Overall Acceptance Rate 213 of 621 submissions, 34%

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