語(yǔ)義網(wǎng)絡(luò)的概念視圖指的是利用軟件代理處理文檔網(wǎng)絡(luò)從而挖掘出網(wǎng)絡(luò)信息。為了在現(xiàn)有網(wǎng)絡(luò)上實(shí)現(xiàn)某些目標(biāo),語(yǔ)義注釋和本體集成起著至關(guān)重要的作用。然而,最近的研究表明,由于一些問(wèn)題語(yǔ)義網(wǎng)絡(luò)尚未被完全建立。其中最重要的是語(yǔ)義網(wǎng)絡(luò)大數(shù)據(jù)的自動(dòng)語(yǔ)義本體集成和語(yǔ)義注釋。為了克服這些問(wèn)題,本文提出一個(gè)自動(dòng)化語(yǔ)義本體集成、大數(shù)據(jù)定位和語(yǔ)義注釋的框架。同一領(lǐng)域的多本體的使用可能會(huì)引起本體之間的異質(zhì)性問(wèn)題。本體集成為異質(zhì)性問(wèn)題提供了解決方案。本文研究了引起異質(zhì)性問(wèn)題的本體集成中的概念匹配過(guò)程,并提出了一種概念匹配的自動(dòng)語(yǔ)義比較方法。該方法采用自然語(yǔ)言處理技術(shù),避免了概念語(yǔ)義匹配中的詞匯或語(yǔ)料庫(kù),而這是當(dāng)前最優(yōu)方法的主要局限性。因此,這項(xiàng)工作的目的是建立一個(gè)更強(qiáng)大的智能最先進(jìn)的系統(tǒng),用于本體集成。另一方面,有關(guān)面向業(yè)務(wù)的系統(tǒng)的大多數(shù)數(shù)據(jù)仍然基于NoSQL或關(guān)系數(shù)據(jù)模型。當(dāng)前,語(yǔ)義網(wǎng)絡(luò)數(shù)據(jù)模型RDF已成為數(shù)據(jù)建模和分析的新標(biāo)準(zhǔn)�；谶@種情況,NoSQL的集成、RDB和RDF數(shù)據(jù)模型正在成為系統(tǒng)的一項(xiàng)必需的功能。在本研究中,我們的目標(biāo)是比較和映射數(shù)據(jù)模型,用于轉(zhuǎn)換NoSQL,RDB和語(yǔ)義網(wǎng)絡(luò)。這項(xiàng)研究將有助于在使用語(yǔ)...

【文章頁(yè)數(shù)】：106 頁(yè)

【文章目錄】：
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
作者簡(jiǎn)歷及在學(xué)研究成果
學(xué)位論文數(shù)據(jù)集



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