本文選題：基礎(chǔ)地理信息本體 + 水文GIS��； 參考：《東華理工大學(xué)》2017年碩士論文

【摘要】：隨著我國科學(xué)新技術(shù)的興起,我國城市形態(tài)也在不斷的改革轉(zhuǎn)型,數(shù)字城市向智慧城市成功升級。以數(shù)字城市為基礎(chǔ),依托“互聯(lián)網(wǎng)+”平臺,爆炸式的信息融入到我們的生活中。智慧城市將帶來一場數(shù)據(jù)集成、共享、互操作的大變革,將各個行業(yè)的數(shù)據(jù),包括行業(yè)的應(yīng)用、行業(yè)的專題數(shù)據(jù)、行政審批數(shù)據(jù),以及物聯(lián)網(wǎng)的手機(jī)、視頻、定位等等相關(guān)的數(shù)據(jù)往地理空間上面進(jìn)行集成將是時下熱點(diǎn)。數(shù)據(jù)呈現(xiàn)大數(shù)據(jù)的形態(tài),面向越來越大的數(shù)據(jù)量,數(shù)據(jù)雖“大”但是并沒有體現(xiàn)其應(yīng)有的價值。不同的領(lǐng)域在各自信息化發(fā)展中都逐漸形成各自行業(yè)體系及相對獨(dú)立的數(shù)據(jù)體系,對于同一內(nèi)容,由于領(lǐng)域的不同導(dǎo)致數(shù)據(jù)概念有所差異。如何實現(xiàn)不同領(lǐng)域、不同系統(tǒng)間的數(shù)據(jù)互操作,以便實現(xiàn)數(shù)據(jù)的重用、語義映射是當(dāng)前亟需解決的問題之一。在智慧城市的總體戰(zhàn)略部署下,不同組織和結(jié)構(gòu)下的信息共享與交流是智慧城市建設(shè)的數(shù)據(jù)基礎(chǔ),地理信息數(shù)據(jù)是用來實現(xiàn)地理智能服務(wù)的重要基礎(chǔ)數(shù)據(jù)之一。隨著本體語義和語義相似度的研究逐漸深入,給跨領(lǐng)域本體間語義映射問題的解決帶來了新的思路。本文以基礎(chǔ)地理信息與水文GIS本體的語義映射為研究對象,提出了一種面向地理實體的語義相似度計算與映射模型,利用改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)深度學(xué)習(xí)的方式來模擬人類主觀評價概念相似度的思維過程,通過改進(jìn)1)優(yōu)化神經(jīng)網(wǎng)絡(luò)算法的全局搜索能力,力求尋找最優(yōu)點(diǎn),2)通過遺傳操作中的選擇算子、交叉算子、變異算子來優(yōu)化神經(jīng)網(wǎng)絡(luò)算法每個神經(jīng)元間的連接權(quán)值和閾值,3)解決不同本體非線性關(guān)系之間的映射問題。這些改進(jìn)均進(jìn)一步提升了傳統(tǒng)語義相似度計算模式,并基于該模型實現(xiàn)對基礎(chǔ)地理信息本體與水文GIS本體的跨領(lǐng)域的語義映射試驗。實驗結(jié)果顯示,改進(jìn)的算法精度優(yōu)于傳統(tǒng)BP算法,同時對改進(jìn)模型進(jìn)行評價,也證實該方式切實有效,可以輔助實現(xiàn)跨領(lǐng)域?qū)ο�、實體的映射、融合、共享。
[Abstract]:With the rise of new science and technology in our country, the urban form of our country is constantly reforming and transforming, and the digital city has been upgraded successfully to the intelligent city. Based on the digital city, relying on the "Internet" platform, explosive information into our lives. Smart City will bring about a big change in data integration, sharing, and interoperability, bringing data from all sectors, including industry applications, industry thematic data, administrative approval data, mobile phones and video from the Internet of things. Positioning and other related data to the geospatial integration will be a hot spot. The data presents big data's form, facing the increasing amount of data, although the data is "large", it does not reflect its due value. Different fields have gradually formed their own industry systems and relatively independent data systems in the development of their respective information systems. For the same content, the different fields lead to different data concepts. How to realize data interoperability in different fields and systems in order to realize data reuse is one of the problems that need to be solved urgently. Under the overall strategic deployment of the intelligent city, the information sharing and communication under different organizations and structures is the data base of the construction of the intelligent city, and the geographic information data is one of the important basic data used to realize the geographic intelligent service. With the further research of ontology semantics and semantic similarity, a new way of thinking has been brought to the solution of semantic mapping problem between different domains. In this paper, the semantic mapping of basic geographic information and hydrological GIS ontology is taken as the research object, and a semantic similarity calculation and mapping model for geographic entities is proposed. Using the improved BP neural network depth learning method to simulate the human subjective evaluation concept similarity thinking process, by improving 1) optimize the global search ability of the neural network algorithm. We try to find the best point by optimizing the connection weight and threshold value of each neuron in the neural network algorithm by using the selection operator, crossover operator and mutation operator in the genetic operation) to solve the mapping problem between different ontology nonlinear relations. These improvements further enhance the traditional semantic similarity computing model and implement cross-domain semantic mapping experiments between the basic geographic information ontology and the hydrological GIS ontology based on the model. The experimental results show that the accuracy of the improved algorithm is better than that of the traditional BP algorithm. At the same time, the improved model is evaluated. It is also proved that this method is effective and can help to realize the mapping, fusion and sharing of cross-domain objects and entities.
【學(xué)位授予單位】：東華理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P208

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 徐英卓;賈歡;;基于樹結(jié)構(gòu)的本體概念相似度計算方法[J];計算機(jī)系統(tǒng)應(yīng)用;2017年03期

2 王占禮;席萍;李靜;朱丹;;遺傳算法優(yōu)化的BP網(wǎng)絡(luò)在銑削力預(yù)測中的應(yīng)用[J];制造業(yè)自動化;2014年14期

3 李軍利;何宗宜;柯棟梁;朱喬利;;一種描述邏輯的地理本體融合方法[J];武漢大學(xué)學(xué)報(信息科學(xué)版);2014年03期

4 許兆美;周建忠;黃舒;孟憲凱;韓煜航;田清;;基于遺傳算法優(yōu)化反向傳播神經(jīng)網(wǎng)絡(luò)的激光銑削層質(zhì)量預(yù)測[J];中國激光;2013年06期

5 劉耀林;王華;唐旭;劉中秋;;省級土地整治項目動態(tài)監(jiān)管業(yè)務(wù)本體及工作流模型構(gòu)建與實現(xiàn)[J];武漢大學(xué)學(xué)報(信息科學(xué)版);2013年04期

6 劉立鵬;王偉;董培欣;魏艷紅;;基于遺傳神經(jīng)網(wǎng)絡(luò)的焊接接頭力學(xué)性能預(yù)測系統(tǒng)[J];焊接學(xué)報;2011年07期

7 李霖;朱海紅;王紅;李德仁;;基于形式本體的基礎(chǔ)地理信息語義分析——以陸地水系要素類為例[J];測繪學(xué)報;2008年02期

8 陳建軍;周成虎;王敬貴;;地理本體的研究進(jìn)展與分析[J];地學(xué)前緣;2006年03期

9 黃茂軍,杜清運(yùn),吳運(yùn)超,李鳳丹;地理本體及其應(yīng)用初探[J];地理與地理信息科學(xué);2004年04期

10 吉根林;遺傳算法研究綜述[J];計算機(jī)應(yīng)用與軟件;2004年02期

相關(guān)碩士學(xué)位論文 前6條

1 李飛;基于本體的概念語義相似度測量研究[D];廣西師范大學(xué);2016年

2 唐中林;基于本體的概念相似度計算方法的研究[D];武漢理工大學(xué);2013年

3 張路;本體映射中概念語義相似度計算方法研究[D];蘭州理工大學(xué);2010年

4 曹道友;基于改進(jìn)遺傳算法的應(yīng)用研究[D];安徽大學(xué);2010年

5 李艷;基于BP神經(jīng)網(wǎng)絡(luò)的本體映射方法研究[D];山東科技大學(xué);2008年

6 李紅梅;地理空間實體類型語義相似度計算模型的研究[D];武漢大學(xué);2005年

，

本文編號：1823233