Digital mine is the only way for the development of mining industry in China. Due to lack of appropriate standards and norms, and different awareness in the field of digital mine among academia and industry insiders, ...Digital mine is the only way for the development of mining industry in China. Due to lack of appropriate standards and norms, and different awareness in the field of digital mine among academia and industry insiders, the meaning for digital mine is still unclear. Starting from the nature of mining and removing of views of specialized fields, this paper constructs formal ontology for digital mine and proposes the four levels for it. The ontology clarifies the concept world for digital mine, defines the meaning of concepts and relations clearly, provides a reference for the standard construction for digital mine and provides a unified semantic framework for the integration of heterogeneous mine data. Meanwhile, it can provide formal reasoning knowledge for expert system of digital mine and improve the intelligence and automation while the machine automatically interpreting and processing mine spatial data.展开更多
An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the...An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.展开更多
Formal methods use mathematical models to develop systems.Ontologies are formal specifications that provide reusable domain knowledge representations.Ontologies have been successfully used in several data-driven appli...Formal methods use mathematical models to develop systems.Ontologies are formal specifications that provide reusable domain knowledge representations.Ontologies have been successfully used in several data-driven applications,including data analysis.However,the creation of formal models from informal requirements demands skill and effort.Ambiguity,inconsistency,imprecision,and incompleteness are major problems in informal requirements.To solve these problems,it is necessary to have methods and approaches for supporting the mapping of requirements to formal specifications.The purpose of this paper is to present an approach that addresses this challenge by using theWeb Ontology Language(OWL)to construct Event-B formal models and support data analysis.Our approach reduces the burden of working with the formal notations of OWL ontologies and Event-B models and aims to analyze domain knowledge and construct Event-B models from OWL ontologies using visual diagrams.The idea is based on the transformation of OntoGraf diagrams of OWL ontologies to UML-B diagrams for the purpose of bridging the gap between OWL ontologies and Event-B models.Visual data exploration assists with both data analysis and the development of Event-B formal models.To manage complexity,Event-B supports stepwise refinement to allow each requirement to be introduced at themost appropriate stage in the development process.UML-B supports refinement,so we also introduce an approach that allows us to divide and layer OntoGraf diagrams.展开更多
基金Project(41001226)supported by the National Natural Science Foundation of ChinaProject(2009CB226107)supported by the National Basic Research Program of China+1 种基金Project(2010B170006)supported by the Natural Science Foundation of Education Department of Henan Province,ChinaProject(KLM201007)supported by Key Laboratory of Mine Spatial Information Technologies,National Administration of Surveying,Mapping and Geoinformation
文摘Digital mine is the only way for the development of mining industry in China. Due to lack of appropriate standards and norms, and different awareness in the field of digital mine among academia and industry insiders, the meaning for digital mine is still unclear. Starting from the nature of mining and removing of views of specialized fields, this paper constructs formal ontology for digital mine and proposes the four levels for it. The ontology clarifies the concept world for digital mine, defines the meaning of concepts and relations clearly, provides a reference for the standard construction for digital mine and provides a unified semantic framework for the integration of heterogeneous mine data. Meanwhile, it can provide formal reasoning knowledge for expert system of digital mine and improve the intelligence and automation while the machine automatically interpreting and processing mine spatial data.
文摘An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.
基金This work was supported by Taif University Researchers Supporting Project Number(TURSP-2020/292),Taif University,Taif,Saudi Arabia.
文摘Formal methods use mathematical models to develop systems.Ontologies are formal specifications that provide reusable domain knowledge representations.Ontologies have been successfully used in several data-driven applications,including data analysis.However,the creation of formal models from informal requirements demands skill and effort.Ambiguity,inconsistency,imprecision,and incompleteness are major problems in informal requirements.To solve these problems,it is necessary to have methods and approaches for supporting the mapping of requirements to formal specifications.The purpose of this paper is to present an approach that addresses this challenge by using theWeb Ontology Language(OWL)to construct Event-B formal models and support data analysis.Our approach reduces the burden of working with the formal notations of OWL ontologies and Event-B models and aims to analyze domain knowledge and construct Event-B models from OWL ontologies using visual diagrams.The idea is based on the transformation of OntoGraf diagrams of OWL ontologies to UML-B diagrams for the purpose of bridging the gap between OWL ontologies and Event-B models.Visual data exploration assists with both data analysis and the development of Event-B formal models.To manage complexity,Event-B supports stepwise refinement to allow each requirement to be introduced at themost appropriate stage in the development process.UML-B supports refinement,so we also introduce an approach that allows us to divide and layer OntoGraf diagrams.
