Affine Transformation Based Ontology Sparse Vector Learning Algorithm-Reference-Cited by-同舟云学术

Affine Transformation Based Ontology Sparse Vector Learning Algorithm

Published:2017-01-01 Issue:1 Volume:2 Page:111-122
ISSN:2444-8656
Container-title:Applied Mathematics and Nonlinear Sciences
language:en
Short-container-title:

Author:

Zhu Linli¹²,Pan Yu¹,Wang Jiangtao³

Affiliation:

1. School of Computer Engineering, Jiangsu University of Technology , Changzhou , Jiangsu 213001 , China

2. Jiangsu Key Laboratory of Recycling and Reuse Technology for Mechanical and Electronic Products , Changzhou , Jiangsu 215500 , China

3. School of Materials and Engineering , Jiangsu University of Technology , Changzhou , Jiangsu 213001 , China

Abstract

Abstract In information science and other engineering applications, ontology plays an irreplaceable role to find the intrinsic semantic link between concepts and to determine the similarity score returned to the user. Ontology mapping aims to excavate the intrinsic semantic relationship between concepts from different ontologies, and the essence of these applications is similarity computation. In this article, we propose the new ontology sparse vector approximation algorithms based on the affine transformation tricks. By means of these techniques, we study the equivalent form of ontology dual problem and determine its feasible set. The simulation experiments imply that our new proposed ontology algorithm has high efficiency and accuracy in ontology similarity computation and ontology mapping in biology, chemical and related fields.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Link

https://www.sciendo.com/pdf/10.21042/AMNS.2017.1.00009

Reference32 articles.

1. C. Betancur, R. Cruz and J. Rada, (2015), Vertex-degree-based topological indices over starlike trees, Discrete Applied Mathematics, 185, 18-25. 10.1016/j.dam.2014.12.021

2. C. Coronnello, M. Tumminello and S. Miccichè, (2016), Gene-based and semantic structure of the GeneOntology as a complex network, Physica A: Statistical Mechanics and its Applications, 458, 313-328. 10.1016/j.physa.2016.03.062

3. U. S. Vishnu, J. Sankarasubramanian, P. Gunasekaran, J. Sridhar and J. Rajendhran, (2016), Omics of Brucella:Species-Specific sRNA-Mediated Gene Ontology Regulatory Networks Identified by Computational Biology, OMICS: A Journal of Integrative Biology, 20, No 6, 375-385. 10.1089/omi.2016.0034

4. M. Roantree, J. O’Donoghue, N. O’Kelly, M. Pierce, K. Irving, M. Van Boxtel and S. Kohler, (2016), Mappinglongitudinal studies to risk factors in an ontology for dementia, Health Informatics Journal, 22, No 2, 414-426. 10.1177/1460458214564092

5. H.-I. Kim and Y.-J. Park, (2016), DNA Microarray and Gene Ontology Enrichment Analysis Reveals That a Mutationin opsX Affects Virulence and Chemotaxis in Xanthomonas oryzae pv. oryzae, The Plant Pathology Journal, 32, No 3, 190-200. 10.5423/PPJ.OA.10.2015.0208

Cited by 13 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. Target Detection of Ground Penetrating Radar Actual Road Anomaly Data;2023 Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC);2023-11-10

2. Recognition Method for Face Local Features Based on Fuzzy Algorithm and Intelligent Data Analysis;Discrete Dynamics in Nature and Society;2022-09-16

3. Design of Heterogeneous Data Transmission System for Industrial Enterprises Based on Fuzzy Algorithm;Wireless Personal Communications;2021-07-02

4. Coverage hole detection method of wireless sensor network based on clustering algorithm;Measurement;2021-07

5. Automatic detection and precise location of texture image defect based on ambiguity resolution algorithm;Journal of Intelligent & Fuzzy Systems;2020-06-25