發(fā)布時(shí)間：2018-03-01 11:17

  本文關(guān)鍵詞： 關(guān)聯(lián)數(shù)據(jù) 知識(shí)元鏈接 圖式存儲(chǔ) 知識(shí)元抽取　出處：《華中師范大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著信息技術(shù)和網(wǎng)絡(luò)技術(shù)的迅速發(fā)展,用戶的知識(shí)信息需求發(fā)生了深刻變革。從用戶認(rèn)知的角度來看,通過鏈接方式獲取所需信息,符合用戶通過聯(lián)想方式獲取信息的本質(zhì),因此,以知識(shí)元鏈接為核心的知識(shí)服務(wù)逐步成為用戶獲取知識(shí)信息的重要手段。目前,以關(guān)系-實(shí)體模型對(duì)知識(shí)元鏈接數(shù)據(jù)進(jìn)行的存儲(chǔ)存在一些不足,首先,數(shù)據(jù)表中數(shù)據(jù)之間的聯(lián)系性很難體現(xiàn)出來,這是由E-R模型本身的特點(diǎn)所決定的；其次,E-R模型只強(qiáng)調(diào)數(shù)據(jù)的靜態(tài)組織,數(shù)據(jù)與其操作是分離的,缺少對(duì)數(shù)據(jù)完整性描述；再次,E-R模型無法對(duì)實(shí)體之間的各種聯(lián)系進(jìn)行進(jìn)一步的約束,最后,查詢語言不能有效地挖掘數(shù)據(jù)之間存在的語義鏈接關(guān)系。以上E-R模型存在的缺點(diǎn)和不足將嚴(yán)重影響數(shù)據(jù)存儲(chǔ)效率。 近年來,隨著關(guān)聯(lián)數(shù)據(jù)技術(shù)研究的不斷深入和成熟,使用關(guān)聯(lián)數(shù)據(jù)技術(shù)對(duì)知識(shí)元鏈接數(shù)據(jù)進(jìn)行語義表示,進(jìn)而為實(shí)現(xiàn)機(jī)器可讀的、鏈接更豐富的知識(shí)元提供了有效的途徑和方法。文章結(jié)合關(guān)聯(lián)數(shù)據(jù)發(fā)布的四項(xiàng)基本原則,通過RDF數(shù)據(jù)模型,將非結(jié)構(gòu)化的、異構(gòu)的知識(shí)元對(duì)象按照統(tǒng)一的標(biāo)準(zhǔn)進(jìn)行一致性轉(zhuǎn)化,最終將這些數(shù)據(jù)以原有的知識(shí)鏈接圖的形式存儲(chǔ)于圖數(shù)據(jù)庫中。本文提出的基于關(guān)聯(lián)數(shù)據(jù)的知識(shí)元鏈接圖式存儲(chǔ)模型研究是對(duì)傳統(tǒng)知識(shí)元鏈接存儲(chǔ)模式的一種改進(jìn),能夠有效實(shí)現(xiàn)關(guān)聯(lián)數(shù)據(jù)環(huán)境下知識(shí)元鏈接的圖式存儲(chǔ)。 本文以關(guān)聯(lián)數(shù)據(jù)為視角,對(duì)知識(shí)元鏈接的圖式存儲(chǔ)模型構(gòu)建理論與方法進(jìn)行了探討。首先對(duì)知識(shí)元鏈接、知識(shí)元抽取、圖式存儲(chǔ)和關(guān)聯(lián)數(shù)據(jù)相關(guān)概念及理論進(jìn)行綜述,然后探討知識(shí)元結(jié)構(gòu)的分析與確定、知識(shí)元抽取的方法、知識(shí)元標(biāo)引及關(guān)聯(lián)數(shù)據(jù)在知識(shí)元鏈接圖式存儲(chǔ)中的作用。其次,在分析傳統(tǒng)知識(shí)元鏈接存儲(chǔ)模式存在不足的基礎(chǔ)上,提出關(guān)聯(lián)數(shù)據(jù)環(huán)境下知識(shí)元鏈接圖式存儲(chǔ)模型,并從知識(shí)元抽取模塊、知識(shí)元鏈接語義描述模塊、語義知識(shí)元鏈接存儲(chǔ)模塊和語義知識(shí)元鏈接圖式存儲(chǔ)應(yīng)用模塊四個(gè)方面對(duì)該模型進(jìn)行詳細(xì)闡述。最后,以科技文獻(xiàn)知識(shí)元為實(shí)驗(yàn)數(shù)據(jù)來源,對(duì)該模型進(jìn)行實(shí)驗(yàn)驗(yàn)證,論證該模型的可行性。
[Abstract]:With the rapid development of information technology and network technology, profound changes have taken place in the knowledge and information needs of users. Users from a cognitive point of view, to obtain the required information through the link, consistent with the nature, access to information users through the associative way therefore, knowledge service knowledge element link as the core has gradually become an important means to acquire knowledge and information users at present, the knowledge element linking data to relational entity model storage has some shortcomings, first of all, the connection between the data in the table is difficult to be reflected, which is decided by the characteristics of E-R model by E-R model; secondly, emphasize the static data organization, data and operation isolation, lack of data integrity; thirdly, E-R model to various links between entities are further constrained, finally, query language can not effectively tap number The shortcomings and shortcomings of the above E-R model will seriously affect the efficiency of data storage.
In recent years, with the deepening of research on data association and mature, using the related data of knowledge element link data semantic representation, then in order to realize the machine readable, knowledge element link provides more effective ways and methods. According to the four basic principles of linked data released by RDF, data model will, unstructured, heterogeneous knowledge element object in accordance with the unified standard consistency transformation, finally these data will be stored in the form of original knowledge link diagram in graph databases. In this paper, the research of related data storage model based on schema knowledge element link is an improvement to the traditional storage mode of knowledge element link the association can effectively achieve the data environment of knowledge element link storage schema.
Based on the related data from the perspective of schema storage model of knowledge element link construction theory and method are discussed. Firstly, knowledge element link, knowledge element extraction, review the schema storage and associated data related concepts and theories, then discusses the analysis and determination of the structure of knowledge element, knowledge element extraction method, the role of knowledge element the indexing and the associated data in the gragh storage of knowledge element link. Secondly, based on the analysis of the traditional storage mode of knowledge element link problems, put forward gragh storage model of knowledge element link correlation data environment, and from the knowledge element extraction module, knowledge element linking semantic description module, the four aspects of the semantic knowledge element link storage module and the semantic knowledge element linking schema storage application module in detail of the model. Finally, the science and technology literature knowledge element as the source of the experimental data, the model is real Verify the feasibility of the model.

【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP311.13;TP333

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