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Harnessing ontology and machine learning for RSO classification

View Article: PubMed Central - PubMed

ABSTRACT

Classification is an important part of resident space objects (RSOs) identification, which is a main focus of space situational awareness. Owing to the absence of some features caused by the limited and uncertain observations, RSO classification remains a difficult task. In this paper, an ontology for RSO classification named OntoStar is built upon domain knowledge and machine learning rules. Then data describing RSO are represented by OntoStar. A demo shows how an RSO is classified based on OntoStar. It is also shown in the demo that traceable and comprehensible reasons for the classification can be given, hence the classification can be checked and validated. Experiments on WEKA show that ontology-based classification gains a relatively high accuracy and precision for classifying RSOs. When classifying RSOs with imperfect data, ontology-based classification keeps its performances, showing evident advantages over classical machine learning classifiers who either have increases of 5 % at least in FP rate or have decreases of 5 % at least in indexes such as accuracy, precision and recall.

No MeSH data available.


General architecture and computational process of OBC
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Fig1: General architecture and computational process of OBC

Mentions: In most applications of OBC, classifications are mainly realized through instance classification, which is based on two processes. First, the ontology for OBC is built. Then, when a new object is created or its property values are modified in the ontology, a reasoning process is applied to the ontology to find matches to the descriptions of the object, and determines the class(s) which the new object belongs to. The architecture and computational process of OBC are generally similar to the description shown in Fig. 1.Fig. 1


Harnessing ontology and machine learning for RSO classification
General architecture and computational process of OBC
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037103&req=5

Fig1: General architecture and computational process of OBC
Mentions: In most applications of OBC, classifications are mainly realized through instance classification, which is based on two processes. First, the ontology for OBC is built. Then, when a new object is created or its property values are modified in the ontology, a reasoning process is applied to the ontology to find matches to the descriptions of the object, and determines the class(s) which the new object belongs to. The architecture and computational process of OBC are generally similar to the description shown in Fig. 1.Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

Classification is an important part of resident space objects (RSOs) identification, which is a main focus of space situational awareness. Owing to the absence of some features caused by the limited and uncertain observations, RSO classification remains a difficult task. In this paper, an ontology for RSO classification named OntoStar is built upon domain knowledge and machine learning rules. Then data describing RSO are represented by OntoStar. A demo shows how an RSO is classified based on OntoStar. It is also shown in the demo that traceable and comprehensible reasons for the classification can be given, hence the classification can be checked and validated. Experiments on WEKA show that ontology-based classification gains a relatively high accuracy and precision for classifying RSOs. When classifying RSOs with imperfect data, ontology-based classification keeps its performances, showing evident advantages over classical machine learning classifiers who either have increases of 5 % at least in FP rate or have decreases of 5 % at least in indexes such as accuracy, precision and recall.

No MeSH data available.