自动本体集成与语义网的语义注释
发布时间：2024-03-04 17:33
语义网络的概念视图指的是利用软件代理处理文档网络从而挖掘出网络信息。为了在现有网络上实现某些目标,语义注释和本体集成起着至关重要的作用。然而,最近的研究表明,由于一些问题语义网络尚未被完全建立。其中最重要的是语义网络大数据的自动语义本体集成和语义注释。为了克服这些问题,本文提出一个自动化语义本体集成、大数据定位和语义注释的框架。同一领域的多本体的使用可能会引起本体之间的异质性问题。本体集成为异质性问题提供了解决方案。本文研究了引起异质性问题的本体集成中的概念匹配过程,并提出了一种概念匹配的自动语义比较方法。该方法采用自然语言处理技术,避免了概念语义匹配中的词汇或语料库,而这是当前最优方法的主要局限性。因此,这项工作的目的是建立一个更强大的智能最先进的系统,用于本体集成。另一方面,有关面向业务的系统的大多数数据仍然基于NoSQL或关系数据模型。当前,语义网络数据模型RDF已成为数据建模和分析的新标准。基于这种情况,NoSQL的集成、RDB和RDF数据模型正在成为系统的一项必需的功能。在本研究中,我们的目标是比较和映射数据模型,用于转换NoSQL,RDB和语义网络。这项研究将有助于在使用语...
【文章页数】：106 页
【文章目录】：
Acknowledgement
摘要
Abstract
1 Introduction
1.1 Study Background
1.1.1 Web 1.0
1.1.2 Web 2.0
1.1.3 Web 3.0
1.1.4 Web 4.0
1.2 Data Schemas and Lakes
1.3 Data Distribution in Big Data
1.4 Significance of Research
2 Literature Review
2.1 Types of Ontology
2.1.1 Top Level Ontology
2.1.2 Domain Ontology
2.1.3 Task Ontology
2.1.4 Domain Task Ontology
2.1.5 Method Ontology
2.1.6 Application Ontologies
2.2 Ontology in Web 3.0
2.3 Concept Matching
2.3.1 Concept Matching Techniques
2.3.2 Overview of Matching Tools
2.4 Concept Matching Systems
2.5 Knowledge Discovery in Databases (KDD)
2.6 Big Data Management
2.7 Mixing Relational Databases with Semantic Web
2.7.1 Data Semantic Annotation
2.8 XML for Web
2.8.1 XMLS for Web
2.8.2 RDF Specifications for W3C
2.8.3 Complex RDF Statements
2.8.4 RDFS Constructs
3 Automated Ontology Integration for Semantic Web
3.1 Introduction
3.2 Research Framework/Proposed Approach
3.2.1 Preprocessing
3.2.2 Similar Concept Identifier
3.2.3 Taxonomy Matching
3.3 Evaluation
3.3.1 Metrics
3.3.2 Dataset
3.3.3 Process
3.3.4 Experiments
3.3.5 Proposed Matched Concepts of Sample Test 1
3.3.6 Sample Test 1 Observations
3.4 Results
3.4.1 Performance Analysis of ASCM
3.5 Summary
4 Data Modeling and Semantic Annotation for Semantic Web
4.1 Introduction
4.2 Research Framework
4.3 Case Study
4.3.1 Data Transformation of RDBS into XMLS
4.3.2 Transformation from XML Schema to RDFS
4.4 Results and Discussion
4.5 Summary
5 Keywords Extraction for Ontology Building
5.1 Introduction
5.2 Background of Study
5.3 Proposed Approach
5.4 Experimental Results
6 Conclusion & Future Work
References
作者简历及在学研究成果
学位论文数据集
本文编号：3918990
【文章页数】：106 页
【文章目录】：
Acknowledgement
摘要
Abstract
1 Introduction
1.1 Study Background
1.1.1 Web 1.0
1.1.2 Web 2.0
1.1.3 Web 3.0
1.1.4 Web 4.0
1.2 Data Schemas and Lakes
1.3 Data Distribution in Big Data
1.4 Significance of Research
2 Literature Review
2.1 Types of Ontology
2.1.1 Top Level Ontology
2.1.2 Domain Ontology
2.1.3 Task Ontology
2.1.4 Domain Task Ontology
2.1.5 Method Ontology
2.1.6 Application Ontologies
2.2 Ontology in Web 3.0
2.3 Concept Matching
2.3.1 Concept Matching Techniques
2.3.2 Overview of Matching Tools
2.4 Concept Matching Systems
2.5 Knowledge Discovery in Databases (KDD)
2.6 Big Data Management
2.7 Mixing Relational Databases with Semantic Web
2.7.1 Data Semantic Annotation
2.8 XML for Web
2.8.1 XMLS for Web
2.8.2 RDF Specifications for W3C
2.8.3 Complex RDF Statements
2.8.4 RDFS Constructs
3 Automated Ontology Integration for Semantic Web
3.1 Introduction
3.2 Research Framework/Proposed Approach
3.2.1 Preprocessing
3.2.2 Similar Concept Identifier
3.2.3 Taxonomy Matching
3.3 Evaluation
3.3.1 Metrics
3.3.2 Dataset
3.3.3 Process
3.3.4 Experiments
3.3.5 Proposed Matched Concepts of Sample Test 1
3.3.6 Sample Test 1 Observations
3.4 Results
3.4.1 Performance Analysis of ASCM
3.5 Summary
4 Data Modeling and Semantic Annotation for Semantic Web
4.1 Introduction
4.2 Research Framework
4.3 Case Study
4.3.1 Data Transformation of RDBS into XMLS
4.3.2 Transformation from XML Schema to RDFS
4.4 Results and Discussion
4.5 Summary
5 Keywords Extraction for Ontology Building
5.1 Introduction
5.2 Background of Study
5.3 Proposed Approach
5.4 Experimental Results
6 Conclusion & Future Work
References
作者简历及在学研究成果
学位论文数据集
本文编号：3918990
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