關(guān)聯(lián)課程數(shù)據(jù)構(gòu)建及存儲(chǔ)方法研究
發(fā)布時(shí)間：2019-03-30 23:16
【摘要】：關(guān)聯(lián)數(shù)據(jù)的概念于2006年被提出。用關(guān)聯(lián)數(shù)據(jù)技術(shù)發(fā)布數(shù)據(jù)信息,是實(shí)現(xiàn)數(shù)據(jù)萬(wàn)維網(wǎng)最重要的一步。W3C組織公布的資源描述框架和Web本體語(yǔ)言對(duì)文檔中出現(xiàn)的概念和概念間的關(guān)系做了形式化的定義,使得網(wǎng)絡(luò)數(shù)據(jù)具有了全球通用性和機(jī)器可讀性的特點(diǎn)。關(guān)聯(lián)數(shù)據(jù)的最主要作用是關(guān)聯(lián)創(chuàng)建和數(shù)據(jù)整合。同時(shí),關(guān)聯(lián)數(shù)據(jù)技術(shù)對(duì)電子學(xué)習(xí)系統(tǒng)也提供了富有語(yǔ)義的知識(shí)服務(wù)。關(guān)聯(lián)課程數(shù)據(jù)的構(gòu)建、組織以及知識(shí)管理是未來(lái)電子學(xué)習(xí)領(lǐng)域的重要研究方向。 本文從數(shù)據(jù)轉(zhuǎn)換、關(guān)聯(lián)課程數(shù)據(jù)構(gòu)建、知識(shí)本體構(gòu)建、關(guān)聯(lián)數(shù)據(jù)存儲(chǔ)索引等方面展開(kāi)研究。 (1)在數(shù)據(jù)轉(zhuǎn)換方面,提出了將多種類(lèi)型的教學(xué)資源文檔轉(zhuǎn)換為RDF數(shù)據(jù)的方法,其中創(chuàng)新提出一種表格數(shù)據(jù)轉(zhuǎn)換成關(guān)聯(lián)數(shù)據(jù)的方法,本章提出一種表格文檔轉(zhuǎn)換成關(guān)聯(lián)數(shù)據(jù)的方法,利用LOD數(shù)據(jù)集的語(yǔ)義關(guān)系,生成表中的列題、表元值、列與列之間關(guān)系的相關(guān)候選類(lèi)和實(shí)體,然后進(jìn)行關(guān)聯(lián)推斷,以馬爾科夫網(wǎng)絡(luò)圖模型為框架,計(jì)算相應(yīng)的因子節(jié)點(diǎn)的值,從候選集中選取最佳的類(lèi)和實(shí)體,分配給表格中的列題和表元,最后將其轉(zhuǎn)換成RDF數(shù)據(jù),并和各經(jīng)典數(shù)據(jù)集相關(guān)聯(lián)。 (2)針對(duì)知識(shí)表示的問(wèn)題,提出了關(guān)聯(lián)課程數(shù)據(jù)構(gòu)建方法,其中以計(jì)算機(jī)微機(jī)接口,組成原理等課程為例,構(gòu)建計(jì)算機(jī)硬件課程RDF數(shù)據(jù)集。然后引入了知識(shí)本體的概念,介紹了本體的定義及本體的構(gòu)建方法,知識(shí)本體的元數(shù)據(jù)以及知識(shí)點(diǎn)的認(rèn)知順序,在此基礎(chǔ)上構(gòu)建前序后續(xù)知識(shí)點(diǎn)之間的關(guān)聯(lián),是系統(tǒng)實(shí)現(xiàn)知識(shí)發(fā)現(xiàn)和知識(shí)導(dǎo)航的基礎(chǔ),同時(shí)提供了更為復(fù)雜的檢索功能。由于自然語(yǔ)言和概念表述的多樣性,知識(shí)點(diǎn)之間存在非前序后續(xù)關(guān)系或非映射的關(guān)聯(lián),本文提出一種新的基于內(nèi)置文本匹配的關(guān)聯(lián)方法,將自然語(yǔ)言中相互關(guān)聯(lián),卻無(wú)法用機(jī)器進(jìn)行推斷的相關(guān)知識(shí)點(diǎn),存放在文本匹配表中,通過(guò)行表元的信息讀取,構(gòu)建它們之間的關(guān)聯(lián),為關(guān)聯(lián)課程數(shù)據(jù)增添更多語(yǔ)義聯(lián)接,有助于學(xué)習(xí)者更全面的掌握課程領(lǐng)域知識(shí),提高學(xué)習(xí)效率。 (3)針對(duì)關(guān)聯(lián)數(shù)據(jù)存儲(chǔ)和索引的問(wèn)題,分析目前采用的主要的語(yǔ)義數(shù)據(jù)存儲(chǔ)方式,分析其利弊,提出一個(gè)基于垂直劃分、多索引及屬性表的混合存儲(chǔ)結(jié)構(gòu),提高存儲(chǔ)和查詢(xún)功能。 同時(shí),基于權(quán)威數(shù)據(jù)集的實(shí)驗(yàn)證明了本文算法研究的有效性,實(shí)驗(yàn)結(jié)果表明本文對(duì)于課程關(guān)聯(lián)數(shù)據(jù)的轉(zhuǎn)換、存儲(chǔ)方面所做的深入研究和提出的實(shí)現(xiàn)方案是具有創(chuàng)新性,并且是可行和有效的。
[Abstract]:The concept of relational data was introduced in 2006. Publishing data information with associated data technology is the most important step to implement the data World wide Web. The resource description framework published by W3C organization and the Web ontology language formalize the concept and the relationship between the concepts that appear in the document, and the relationship between the concepts in the document is formally defined by the resource description framework published by the W3C organization and the Web ontology language. The network data has the characteristics of global universality and machine readability. The most important role of associated data is association creation and data consolidation. At the same time, the related data technology also provides semantic knowledge service to the e-learning system. The construction, organization and knowledge management of related curriculum data are important research directions in the field of e-learning in the future. In this paper, data conversion, association curriculum data construction, knowledge ontology construction, relational data storage index and other aspects are studied. The main contents are as follows: (1) in the aspect of data conversion, this paper proposes a method to convert many kinds of teaching resource documents into RDF data, in which a new method of transforming tabular data into related data is proposed. In this chapter, a method of transforming tabular documents into associated data is proposed. By using the semantic relationship of LOD data set, the column title, table element value, candidate classes and entities related to the relationship between columns and columns are generated, and then the correlation inference is made. The Markov network graph model is used as the frame to calculate the value of the corresponding factor node, select the best class and entity from the candidate set, assign it to the column title and table element in the table, and convert it into RDF data, and associate it with each classical data set. (2) aiming at the problem of knowledge representation, this paper puts forward the method of constructing the data of related courses, in which the RDF data set of computer hardware course is constructed by taking the courses of computer interface, composition principle and so on as examples. Then it introduces the concept of knowledge ontology, introduces the definition of ontology and the construction method of ontology, the metadata of knowledge ontology and the cognitive order of knowledge points, on the basis of which, it constructs the relationship between pre-order and follow-up knowledge points. It is the basis of the system to realize knowledge discovery and knowledge navigation, and provides more complex retrieval function at the same time. Due to the diversity of natural language and conceptual representation, there is a non-preorder or non-mapping relationship between knowledge points. This paper proposes a new correlation method based on built-in text matching, which associates natural language with each other. But the related knowledge points that can not be inferred by machine are stored in the text matching table. By reading the information of row table elements, the association between them is constructed, and more semantic joins are added to the related curriculum data. It is helpful for learners to master the knowledge of curriculum field more comprehensively and improve the learning efficiency. (3) aiming at the problem of relational data storage and index, this paper analyzes the main semantic data storage methods adopted at present, analyzes its advantages and disadvantages, and proposes a hybrid storage structure based on vertical partition, multi-index and attribute table, which is based on vertical partition, multi-index and attribute table. Improve storage and query functions. At the same time, the experiment based on authoritative data sets proves the validity of this algorithm. The experimental results show that the in-depth research and implementation of curriculum related data conversion and storage is innovative. And it is feasible and effective.
【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TP391.1;TP333
[Abstract]:The concept of relational data was introduced in 2006. Publishing data information with associated data technology is the most important step to implement the data World wide Web. The resource description framework published by W3C organization and the Web ontology language formalize the concept and the relationship between the concepts that appear in the document, and the relationship between the concepts in the document is formally defined by the resource description framework published by the W3C organization and the Web ontology language. The network data has the characteristics of global universality and machine readability. The most important role of associated data is association creation and data consolidation. At the same time, the related data technology also provides semantic knowledge service to the e-learning system. The construction, organization and knowledge management of related curriculum data are important research directions in the field of e-learning in the future. In this paper, data conversion, association curriculum data construction, knowledge ontology construction, relational data storage index and other aspects are studied. The main contents are as follows: (1) in the aspect of data conversion, this paper proposes a method to convert many kinds of teaching resource documents into RDF data, in which a new method of transforming tabular data into related data is proposed. In this chapter, a method of transforming tabular documents into associated data is proposed. By using the semantic relationship of LOD data set, the column title, table element value, candidate classes and entities related to the relationship between columns and columns are generated, and then the correlation inference is made. The Markov network graph model is used as the frame to calculate the value of the corresponding factor node, select the best class and entity from the candidate set, assign it to the column title and table element in the table, and convert it into RDF data, and associate it with each classical data set. (2) aiming at the problem of knowledge representation, this paper puts forward the method of constructing the data of related courses, in which the RDF data set of computer hardware course is constructed by taking the courses of computer interface, composition principle and so on as examples. Then it introduces the concept of knowledge ontology, introduces the definition of ontology and the construction method of ontology, the metadata of knowledge ontology and the cognitive order of knowledge points, on the basis of which, it constructs the relationship between pre-order and follow-up knowledge points. It is the basis of the system to realize knowledge discovery and knowledge navigation, and provides more complex retrieval function at the same time. Due to the diversity of natural language and conceptual representation, there is a non-preorder or non-mapping relationship between knowledge points. This paper proposes a new correlation method based on built-in text matching, which associates natural language with each other. But the related knowledge points that can not be inferred by machine are stored in the text matching table. By reading the information of row table elements, the association between them is constructed, and more semantic joins are added to the related curriculum data. It is helpful for learners to master the knowledge of curriculum field more comprehensively and improve the learning efficiency. (3) aiming at the problem of relational data storage and index, this paper analyzes the main semantic data storage methods adopted at present, analyzes its advantages and disadvantages, and proposes a hybrid storage structure based on vertical partition, multi-index and attribute table, which is based on vertical partition, multi-index and attribute table. Improve storage and query functions. At the same time, the experiment based on authoritative data sets proves the validity of this algorithm. The experimental results show that the in-depth research and implementation of curriculum related data conversion and storage is innovative. And it is feasible and effective.
【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TP391.1;TP333
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