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(guó)科學(xué)新技術(shù)的興起,我國(guó)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(jí)。以數(shù)字城市為基礎(chǔ),依托“互聯(lián)網(wǎng)+”平臺(tái),爆炸式的信息融入到我們的生活中。智慧城市將帶來(lái)一場(chǎng)數(shù)據(jù)集成、共享、互操作的大變革,將各個(gè)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(shí)下熱點(diǎn)。數(shù)據(jù)呈現(xiàn)大數(shù)據(jù)的形態(tài),面向越來(lái)越大的數(shù)據(jù)量,數(shù)據(jù)雖“大”但是并沒(méi)有體現(xiàn)其應(yīng)有的價(jià)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(duì)獨(dú)立的數(shù)據(jù)體系,對(duì)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(shí)現(xiàn)不同領(lǐng)域、不同系統(tǒng)間的數(shù)據(jù)互操作,以便實(shí)現(xiàn)數(shù)據(jù)的重用、語(yǔ)義映射是當(dāng)前亟需解決的問(wèn)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(lái)實(shí)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(yǔ)義和語(yǔ)義相似度的研究逐漸深入,給跨領(lǐng)域本體間語(yǔ)義映射問(wèn)題的解決帶來(lái)了新的思路。本文以基礎(chǔ)地理信息與水文GIS本體的語(yǔ)義映射為研究對(duì)象,提出了一種面向地理實(shí)體的語(yǔ)義相似度計(jì)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(lái)模擬人類主觀評(píng)價(jià)概念相似度的思維過(guò)程,通過(guò)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(guò)遺傳操作中的選擇算子、交叉算子、變異算子來(lái)優(yōu)化神經(jīng)網(wǎng)絡(luò)算法每個(gè)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(wèn)題。這些改進(jìn)均進(jìn)一步提升了傳統(tǒng)語(yǔ)義相似度計(jì)算模式,并基于該模型實(shí)現(xiàn)對(duì)基礎(chǔ)地理信息本體與水文GIS本體的跨領(lǐng)域的語(yǔ)義映射試驗(yàn)。實(shí)驗(yàn)結(jié)果顯示,改進(jìn)的算法精度優(yōu)于傳統(tǒng)BP算法,同時(shí)對(duì)改進(jìn)模型進(jìn)行評(píng)價(jià),也證實(shí)該方式切實(shí)有效,可以輔助實(shí)現(xiàn)跨領(lǐng)域?qū)ο�、�?shí)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P208

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