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Simulated Annealing-based Ontology Matching

Simulated Annealing-based Ontology Matching Ontology alignment is a fundamental task to reconcile the heterogeneity among various information systems using distinct information sources. The evolutionary algorithms (EAs) have been already considered as the primary strategy to develop an ontology alignment system. However, such systems have two significant drawbacks: they either need a ground truth that is often unavailable, or they utilize the population-based EAs in a way that they require massive computation and memory. This article presents a new ontology alignment system, called SANOM, which uses the well-known simulated annealing as the principal technique to find the mappings between two given ontologies while no ground truth is available. In contrast to population-based EAs, the simulated annealing need not generate populations, which makes it significantly swift and memory-efficient for the ontology alignment problem. This article models the ontology alignment problem as optimizing the fitness of a state whose optimum is obtained by using the simulated annealing. A complex fitness function is developed that takes advantage of various similarity metrics including string, linguistic, and structural similarities. A randomized warm initialization is specially tailored for the simulated annealing to expedite its convergence. The experiments illustrate that SANOM is competitive with the state-of-the-art and is significantly superior to other EA-based systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

Simulated Annealing-based Ontology Matching

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2019 ACM
ISSN
2158-656X
eISSN
2158-6578
DOI
10.1145/3314948
Publisher site
See Article on Publisher Site

Abstract

Ontology alignment is a fundamental task to reconcile the heterogeneity among various information systems using distinct information sources. The evolutionary algorithms (EAs) have been already considered as the primary strategy to develop an ontology alignment system. However, such systems have two significant drawbacks: they either need a ground truth that is often unavailable, or they utilize the population-based EAs in a way that they require massive computation and memory. This article presents a new ontology alignment system, called SANOM, which uses the well-known simulated annealing as the principal technique to find the mappings between two given ontologies while no ground truth is available. In contrast to population-based EAs, the simulated annealing need not generate populations, which makes it significantly swift and memory-efficient for the ontology alignment problem. This article models the ontology alignment problem as optimizing the fitness of a state whose optimum is obtained by using the simulated annealing. A complex fitness function is developed that takes advantage of various similarity metrics including string, linguistic, and structural similarities. A randomized warm initialization is specially tailored for the simulated annealing to expedite its convergence. The experiments illustrate that SANOM is competitive with the state-of-the-art and is significantly superior to other EA-based systems.

Journal

ACM Transactions on Management Information Systems (TMIS) – Association for Computing Machinery

Published: May 9, 2019

Keywords: OAEI

References

  • A comparison of multi-objective evolutionary algorithms for the ontology meta-matching problem
    Acampora, Giovanni; Ishibuchi, Hisao; Vitiello, Autilia
  • Results of the ontology alignment evaluation initiative 2016
    Achichi, Manel; Cheatham, Michelle; Dragisic, Zlatan; Euzenat, Jérôme; Faria, Daniel; Ferrara, Alfio; Flouris, Giorgos; Fundulaki, Irini; Harrow, Ian; Ivanova, Valentina
  • CEL—A polynomial-time reasoner for life science ontologies
    Baader, Franz; Lutz, Carsten; Suntisrivaraporn, Boontawee
  • The ontology for biomedical investigations
    Bandrowski, Anita; Brinkman, Ryan; Brochhausen, Mathias; Brush, Matthew H.; Bug, Bill; Chibucos, Marcus C.; Clancy, Kevin; Courtot, Mélanie; Derom, Dirk; Dumontier, Michel
  • Improvements of general multiple test procedures for redundant systems of hypotheses
    Bergmann, Beate; Hommel, Gerhard
  • Discrete particle swarm optimisation for ontology alignment
    Bock, Jürgen; Hettenhausen, Jan
  • The unified medical language system (UMLS): Integrating biomedical terminology
    Bodenreider, Olivier
  • String similarity metrics for ontology alignment
    Cheatham, Michelle; Hitzler, Pascal
  • The properties of property alignment
    Cheatham, Michelle; Hitzler, Pascal
  • A comparison of string metrics for matching names and records
    Cohen, William; Ravikumar, Pradeep; Fienberg, Stephen
  • ALIN results for OAEI 2017
    Silva, Jomar da; Baiao, Fernanda Araujo; Revoredo, Kate
  • Ontology Matching
    Euzenat, Jérôme; Shvaiko, Pavel
  • Results of AML in OAEI 2017
    Faria, Daniel; Balasubramani, Booma Sowkarthiga; Shivaprabhu, Vivek R.; Mott, Isabela; Pesquita, Catia; Couto, Francisco M.; Cruz, Isabel F.
  • Tackling the challenges of matching biomedical ontologies
    Faria, Daniel; Pesquita, Catia; Mott, Isabela; Martins, Catarina; Couto, Francisco M.; Cruz, Isabel F.
  • Automatic background knowledge selection for matching biomedical ontologies
    Faria, Daniel; Pesquita, Catia; Santos, Emanuel; Cruz, Isabel F.; Couto, Francisco M.
  • Optimizing ontology alignments by using genetic algorithms
    Martinez-Gil, Jorge; Alba, Enrique; Aldana-Montes, José F.
  • Matching disease and phenotype ontologies in the ontology alignment evaluation initiative
    Harrow, Ian; Jiménez-Ruiz, Ernesto; Splendiani, Andrea; Romacker, Martin; Woollard, Peter; Markel, Scott; Alam-Faruque, Yasmin; Koch, Martin; Malone, James; Waaler, Arild
  • WikiV3 results for OAEI 2017
    Hertling, Sven
  • The role of ontologies in biological and biomedical research: A functional perspective
    Hoehndorf, Robert; Schofield, Paul N.; Gkoutos, Georgios V.
  • The OWL API: A Java API for OWL ontologies
    Horridge, Matthew; Bechhofer, Sean
  • Logmap: Logic-based and scalable ontology matching
    Jiménez-Ruiz, Ernesto; Grau, Bernardo Cuenca
  • OAEI 2017 results of KEPLER
    Kachroudi, Marouen; Diallo, Gayo; Yahia, Sadok Ben
  • A Java fork/join framework
    Lea, Doug
  • A genetic algorithms-based approach for optimizing similarity aggregation in ontology matching
    Martínez-Romero, Marcos; Vázquez-Naya, José Manuel; Nóvoa, Francisco Javier; Vázquez, Guillermo; Pereira, Javier
  • Equation of state calculations by fast computing machines
    Metropolis, Nicholas; Rosenbluth, Arianna; Rosenbluth, Marshall; Teller, Augusta; Teller, Edward
  • WordNet: A lexical database for English
    Miller, George A.
  • SANOM results for OAEI 2017
    Mohammadi, Majid; Atashin, Amir; Hofman, Wout; Tan, Yaohua
  • Comparison of ontology alignment systems across single matching task via the McNemar's test
    Mohammadi, Majid; Atashin, Amir Ahooye; Hofman, Wout; Tan, Yaohua
  • A comparative study of ontology matching systems via inferential statistics
    Mohammadi, Majid; Hofman, Wout; Tan, Yao-Hua
  • An algorithm for suffix stripping
    Porter, Martin F.
  • Semantics, ontologies, and eScience for the geosciences
    Reitsma, Femke; Laxton, John; Ballard, Stuart; Kuhn, Werner; Abdelmoty, Alia
  • Feature-rich part-of-speech tagging with a cyclic dependency network
    Toutanova, Kristina; Klein, Dan; Manning, Christopher D.; Singer, Yoram
  • Gaom: Genetic algorithm-based ontology matching
    Wang, Junli; Ding, Zhijun; Jiang, Changjun
  • Verbs semantics and lexical selection
    Wu, Zhibiao; Palmer, Martha
  • Optimizing ontology alignment through hybrid population-based incremental learning algorithm
    Xue, Xingsi; Chen, Junfeng
  • Optimizing ontology alignment by using compact genetic algorithm
    Xue, Xingsi; Liu, Jianhua; Tsai, Pei-Wei; Zhan, Xianyin; Ren, Aihong
  • Compact evolutionary algorithm-based ontology meta-matching
    Xue, Xingsi; Liu, Shijian
  • Optimizing ontology alignments through a memetic algorithm using both MatchFmeasure and unanimous improvement ratio
    Xue, Xingsi; Wang, Yuping
  • Improving the efficiency of NSGA-II based ontology aligning technology
    Xue, Xingsi; Wang, Yuping
  • Optimizing ontology alignments by using NSGA-II
    Xue, Xingsi; Wang, Yuping; Hao, Weichen
  • A normalized Levenshtein distance metric
    Yujian, Li; Bo, Liu