發(fā)布時(shí)間：2018-08-24 18:12

【摘要】：互聯(lián)網(wǎng)時(shí)代,隨著信息技術(shù)的快速發(fā)展,知識(shí)正呈現(xiàn)海量、多源、異構(gòu)化趨勢(shì),如何對(duì)知識(shí)進(jìn)行組織管理從而有效獲取是信息檢索領(lǐng)域的研究熱點(diǎn),本體作為一種新型的知識(shí)組織工具,具有良好表示語(yǔ)義關(guān)系且支持邏輯推理的特點(diǎn),得到廣泛的應(yīng)用。茶是世界三大無(wú)酒精飲料之一,種植區(qū)域遍布全球,中國(guó)作為茶葉發(fā)源地,有著悠久的茶學(xué)研究歷史,茶學(xué)知識(shí)涉及栽培、生物化學(xué)、病蟲害、檢驗(yàn)學(xué)、機(jī)械學(xué)、文化習(xí)俗、產(chǎn)業(yè)經(jīng)濟(jì)等眾多領(lǐng)域,在此技術(shù)和知識(shí)背景下,本文以豐富的茶學(xué)知識(shí)作為研究對(duì)象,采用本體技術(shù)實(shí)現(xiàn)茶學(xué)知識(shí)的組織以及檢索系統(tǒng)的本體應(yīng)用,本文主要可分為三個(gè)部分:第一部分,本文首先對(duì)本體的定義、分類、應(yīng)用進(jìn)行了學(xué)習(xí),又深入了解知識(shí)經(jīng)濟(jì)社會(huì)中組織工具的發(fā)展,對(duì)比分析各個(gè)組織工具的優(yōu)勢(shì)與不足,指出本體在信息組織方面受到重點(diǎn)關(guān)注,由于本文研究對(duì)象為茶學(xué),屬于農(nóng)學(xué)一部分,因此對(duì)農(nóng)學(xué)本體研究現(xiàn)狀也做了調(diào)查分析,本體構(gòu)建理論基礎(chǔ)知識(shí)如構(gòu)建方法、編輯工具和開(kāi)發(fā)工具也都一一進(jìn)行了學(xué)習(xí)以供后續(xù)茶學(xué)本體的構(gòu)建。第二部分,在調(diào)查本體人工構(gòu)建費(fèi)時(shí)費(fèi)力、專家依賴性強(qiáng)等不足后采用本體學(xué)習(xí)方法對(duì)茶學(xué)本體進(jìn)行半自動(dòng)構(gòu)建。在對(duì)本體學(xué)習(xí)方法深入分析后,運(yùn)用本體構(gòu)建方法中的“七步法”和“骨架法”構(gòu)建茶學(xué)本體,首先使用ICTCLAS分詞系統(tǒng)將獲取語(yǔ)料進(jìn)行分詞處理和詞性標(biāo)注,編寫程序完成指定詞性和停用詞的刪除,其次采用TF-IDF方法實(shí)現(xiàn)基于權(quán)重的特征詞篩選來(lái)抽取茶學(xué)概念,獲得候選概念集,并結(jié)合敘詞表、茶葉辭典和領(lǐng)域?qū)＜疫M(jìn)行術(shù)語(yǔ)規(guī)范和補(bǔ)充,然后依據(jù)關(guān)聯(lián)規(guī)則挖掘方法設(shè)定支持度、置信度閾值來(lái)識(shí)別概念間關(guān)系,通過(guò)以上主要步驟獲得茶學(xué)本體相應(yīng)的類、屬性、實(shí)例,利用本體編輯軟件Prot e ge完成形式化表示,主要有類層次的確定、對(duì)象屬性定義域和值域的設(shè)置、數(shù)據(jù)屬性的限制等,并加入本體評(píng)價(jià)與優(yōu)化步驟,由Prot ege自帶HermiT推理機(jī)進(jìn)行邏輯一致性檢測(cè),力證所構(gòu)建茶學(xué)本體的合理性。第三部分,基于茶學(xué)本體實(shí)現(xiàn)知識(shí)檢索方面的應(yīng)用,首先闡述了傳統(tǒng)信息檢索存在的用戶忠實(shí)表達(dá)難、詞形匹配、詞匯孤島的局限性以及知識(shí)檢索所具有的語(yǔ)義匹配、智能推理的優(yōu)勢(shì),其次探討了基于茶學(xué)本體知識(shí)檢索關(guān)鍵技術(shù)的解決,包括擴(kuò)展查詢功能、信息資源標(biāo)引功能、資源檢索功能的實(shí)現(xiàn),具體是運(yùn)用Jena語(yǔ)義包進(jìn)行本體的讀取和解析,Ecl ipse開(kāi)發(fā)工具界面的編寫使得檢索系統(tǒng)在基于關(guān)鍵詞的檢索方法中實(shí)現(xiàn)了同義詞、上位詞、關(guān)系詞的語(yǔ)義擴(kuò)展,提高了一定程度的查全率和查準(zhǔn)率。
[Abstract]:In the Internet era, with the rapid development of information technology, knowledge is showing the trend of mass, multi-source, isomerization. How to organize and manage knowledge to obtain effectively is the research hotspot in the field of information retrieval. As a new knowledge organization tool ontology is widely used because of its good representation of semantic relations and the support of logical reasoning. Tea is one of the three largest non-alcoholic beverages in the world. Tea is grown all over the world. As the birthplace of tea, China has a long history of tea research. Tea knowledge involves cultivation, biochemistry, diseases and insect pests, laboratory studies, mechanics, and cultural practices. Under the background of this technology and knowledge, this paper takes the abundant tea knowledge as the research object, uses the ontology technology to realize the tea science knowledge organization and the retrieval system ontology application. This paper can be divided into three parts: the first part, this paper first of all, the definition of ontology, classification, application of learning, but also in-depth understanding of the development of organizational tools in the knowledge economy society, comparative analysis of the advantages and disadvantages of each organizational tool. It is pointed out that ontology is paid more attention to in the field of information organization. Because the research object of this paper is tea science, it is a part of agronomy. Therefore, the present situation of agricultural ontology research is also investigated and analyzed. The basic knowledge of ontology construction theory such as construction method is also investigated and analyzed. Editing tools and development tools are also studied to follow up the construction of tea ontology. The second part uses ontology learning method to construct tea ontology semi-automatically after investigating the disadvantages of artificial construction and expert dependence. After deeply analyzing the ontology learning methods, using the "seven steps" and "skeleton method" in the ontology construction method to construct the tea ontology, we first use the ICTCLAS word segmentation system to deal with the word segmentation and the part of speech tagging. The program was written to complete the deletion of designated parts of speech and stop words. Secondly, TF-IDF method was used to carry out the feature word selection based on weight to extract tea concept, to obtain candidate concept set, and to combine with thesaurus. The tea dictionaries and domain experts standardize and supplement the terms, then set the support degree and confidence threshold to identify the relationship between concepts according to the association rules mining method, and obtain the corresponding classes, attributes and examples of tea ontology through the above main steps. The formal representation is accomplished by using ontology editing software Prot e ge, which mainly includes the determination of class level, the setting of object attribute domain and range, the limitation of data attribute, and the steps of ontology evaluation and optimization. The logic consistency is checked by Prot ege's own HermiT inference machine, and the rationality of tea ontology is proved. In the third part, the application of knowledge retrieval based on tea ontology is discussed. Firstly, the difficulties of user faithful expression, word shape matching, the limitation of vocabulary island and the semantic matching of traditional information retrieval are expounded. The advantages of intelligent reasoning. Secondly, the key technologies of tea ontology knowledge retrieval are discussed, including the expansion of query function, the indexing of information resources and the realization of resource retrieval. Specifically, Jena semantic package is used for ontology reading and parsing ECL ipse development tool interface, which makes the retrieval system realize the semantic extension of synonyms, upper words and relational words in the retrieval method based on keywords. Improved a certain degree of recall and precision.
【學(xué)位授予單位】：南京農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3

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本文編號(hào)：2201629