.png){kind=icon}
返回
Matching sensor ontologies through siamese neural networks without using reference alignment
.png){kind=icon}
OA
AI
DOI：10.7717/peerj-cs.602.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sensors have been growingly used in a variety of applications. The lack of semantic information of obtained sensor data will bring about the heterogeneity problem of sensor data in semantic, schema, and syntax levels. To solve the heterogeneity problem of sensor data, it is necessary to carry out the sensor ontology matching process to determine correspondences among heterogeneous sensor concepts. In this paper, we propose a Siamese Neural Network based Ontology Matching technique (SNN-OM) to align the sensor ontologies, which does not require the utilization of reference alignment to train the network model. In particular, a representative concepts extraction method is presented to enhance the model's performance and reduce the time of the training process, and an alignment refining method is proposed to enhance the alignments' quality by removing the logically conflict correspondences. The experimental results show that SNN-OM is capable of efficiently determining high-quality sensor ontology alignments.
Keyword:
Sensor Ontology Matching
Siamese Neural Networks
Alignment Refinement
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:
3.4K
被引数:
6.9K
机构
引用论文
Primary leptomeningeal melanoma. Diagnosis by ultrastructural cytology of cerebrospinal fluid and cranial computed tomography.png)
Cancer
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Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges人体传感器网络中的多传感器融合: 最新技术和研究挑战INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏
查看更多内容