Optimizing ontology alignments through a Memetic Algorithm using both MatchFmeasure and Unanimous Improvement Ratio-Reference-Cited by-同舟云学术

Optimizing ontology alignments through a Memetic Algorithm using both MatchFmeasure and Unanimous Improvement Ratio

Published:2015-06 Issue: Volume:223 Page:65-81
ISSN:0004-3702
Container-title:Artificial Intelligence
language:en
Short-container-title:Artificial Intelligence

Author:

Xue Xingsi,Wang Yuping

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference29 articles.

1. Similarity-based ontology alignment in OWL-Lite;Euzenat,2004

2. Combining evaluation metrics via the unanimous improvement ratio and its application to clustering tasks;Amigo;J. Artif. Intell. Res.,2011

3. A Memetic approach for the travelling salesman problem — implementation of a computational ecology for combinatorial optimization on message-passing systems;Moscato,1992

4. Formal memetic algorithms;Radcliffe,1994

5. Memetic algorithms for the traveling salesman problem;Merz;Complex Syst.,2011

Cited by 130 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Efficient large-scale biomedical ontology matching with anchor-based biomedical ontology partitioning and compact geometric semantic genetic programming;Journal of Industrial Information Integration;2024-09

2. Complex Ontology Alignments using OWL Ontologies Embedding;2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA);2023-09-20

3. Intelligent ontology alignment to improve semantic data integration across research domains in biomedicine;2023-08-18

4. Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm;Sensors;2023-05-25

5. Deep Reinforcement Learning Based Ontology Meta-Matching Technique;IEICE Transactions on Information and Systems;2023-05-01