.png){kind=icon}
返回
An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-021-05895-y.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To address the heterogeneity problem of sensor data, it is necessary to conduct the Sensor Ontology Matching (SOM) process to find the mappings among diverse sensor data with the same semantics connotation. Currently, many Multi-Objective Evolutionary Algorithms (MOEAs) have been used to match the ontologies, which aim at finding a set of solutions called Pareto Set (PS) in the Pareto Front (PF) to represent a set of trade-off proposals for different Decision Makers (DMs). Being inspired by the success of MOEA with Inverse Model (IM-MOEA) in solving complicated optimization problems, in this work, an Improved IM-MOEA (I-IM-MOEA)-based matching technique is further proposed to enhance the algorithm's matching efficiency as well as the alignment's quality. To overcome the drawback of IM-MOEA that has poor performance on irregular PF, an adjusted selection mechanism is employed to avert the massive reduction in non-domination solutions on irregular PF, a dynamic Reference Vectors (RVs) is used to decrease the computational resources and boost the efficiency of the algorithm, and a local search strategy is introduced to promote the results' quality. The experiment employs the benchmark provided by Ontology Alignment Evaluation Initiative (OAEI) and three sensor ontologies to assess the performance of I-IM-MOEA, and the experimental results show that I-IM-MOEA is both effective and efficient.
Keyword:
Sensor ontology matching
Multi-objective evolutionary algorithm
Inverse modeling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
Enhanced sequence labeling based on latent variable conditional random fields.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏Primary leptomeningeal melanoma. Diagnosis by ultrastructural cytology of cerebrospinal fluid and cranial computed tomographyCancer
CancerIF0
PREAI
分享收藏Matching large-scale biomedical ontologies with central concept based partitioning algorithm and Adaptive Compact Evolutionary Algorithm基于中心概念划分算法和自适应紧凑进化算法的大规模生物医学本体匹配
PREAI
分享收藏分享收藏Physiological responses and analysis of tolerance of apple rootstocks to root-zone hypoxia stress
IF0
PREAI
分享收藏
查看更多内容