【摘要】：隨著信息技術(shù)的發(fā)展,信息量驟然增大,信息的價(jià)值密度降低,龐大的信息量看似能夠給人們提供更多想要的信息,事實(shí)上卻是使人更難使用信息。因此,各種大數(shù)據(jù)技術(shù)應(yīng)運(yùn)而生,旨在對(duì)海量的數(shù)據(jù)進(jìn)行專業(yè)化處理,通過對(duì)數(shù)據(jù)的獲取、存儲(chǔ)、管理和分析,為人們提供真正需要的信息。空間信息作為海量信息的一種,被應(yīng)用于人類生活的各個(gè)方面。無論是在互聯(lián)網(wǎng)中新聞的推送、電子商務(wù)中廣告的精準(zhǔn)投放,還是導(dǎo)航和交通路況中對(duì)信息的分析,還是災(zāi)難后的政府救援,抑或是城市和企業(yè)發(fā)展中建筑的選址,都離不開大數(shù)據(jù)支持和對(duì)地理空間信息的分析。因此,對(duì)海量數(shù)據(jù)中空間關(guān)系的分析處理顯得尤為重要。空間關(guān)系的研究已經(jīng)有20多年的歷史,空間關(guān)系被廣泛地應(yīng)用于空間推理和空間分析中,國內(nèi)外已有不少學(xué)者做出了許多研究成果�，F(xiàn)有的定性空間關(guān)系模型大多基于健全的代數(shù)系統(tǒng),使用先驗(yàn)知識(shí)人工進(jìn)行建模,現(xiàn)有簡單空間關(guān)系模型不能全面地表達(dá)實(shí)際應(yīng)用中所需要的空間關(guān)系;現(xiàn)有復(fù)雜空間關(guān)系模型僅能針對(duì)特定的復(fù)雜空間關(guān)系進(jìn)行識(shí)別,不具有通用性。用基于健全的代數(shù)系統(tǒng)的方法對(duì)大數(shù)據(jù)中空間關(guān)系人工構(gòu)建空間關(guān)系模型,需要對(duì)空間對(duì)象的先驗(yàn)知識(shí)進(jìn)行分析,需要考慮的元素很多,導(dǎo)致形式化表示系統(tǒng)極其復(fù)雜,甚至難以形式化表示,不能滿足大數(shù)據(jù)時(shí)代中對(duì)空間關(guān)系表達(dá)的需求。為了解決現(xiàn)有空間關(guān)系模型中存在的以上問題,我們對(duì)空間關(guān)系推理方法進(jìn)行了研究,本文提出了將定性空間推理與機(jī)器學(xué)習(xí)相結(jié)合生成空間關(guān)系模型的方法。本文的方法能夠在不需要先驗(yàn)知識(shí)的情況下生成空間關(guān)系模型,具有很好的通用性。本文的主要研究內(nèi)容如下:(1)本文首先對(duì)空間推理中的空間關(guān)系這一研究熱點(diǎn)的在當(dāng)今時(shí)代的研究背景、研究意義以及目前的研究發(fā)展現(xiàn)狀進(jìn)行了闡述,然后介紹了本文的研究內(nèi)容和全文的組織結(jié)構(gòu)。(2)介紹了空間關(guān)系的基本概念和機(jī)器學(xué)習(xí)的相關(guān)知識(shí),并對(duì)MLNB算法進(jìn)行了描述。(3)通過對(duì)現(xiàn)有空間關(guān)系模型中存在的問題的分析,提出了生成空間關(guān)系模型的方法,介紹了該方法的流程。提出了空間關(guān)系的通用特征集合,詳細(xì)介紹了對(duì)空間對(duì)象的區(qū)域分解,子區(qū)域和擴(kuò)展區(qū)域的劃分。并在此基礎(chǔ)上,提出了特征分級(jí)策略,將子區(qū)域分組并組合,以減少處理的特征數(shù)目。(4)將本文提出的生成空間關(guān)系模型的方法應(yīng)用于多個(gè)數(shù)據(jù)集,通過對(duì)比應(yīng)用于不同數(shù)據(jù)集的結(jié)果分析了本文方法的優(yōu)點(diǎn)。將本文方法應(yīng)用于中文文本的空間關(guān)系識(shí)別中,取得了較好的效果。(5)對(duì)全文的內(nèi)容進(jìn)行總結(jié),并提出了對(duì)進(jìn)一步工作的展望。
[Abstract]:With the development of information technology, the amount of information increases suddenly, and the value density of information decreases. The huge amount of information seems to provide more information to people, but in fact, it makes it more difficult for people to use information. Therefore, a variety of big data technologies emerge as the times require, aiming at the specialized processing of massive data. Through the acquisition, storage, management and analysis of the data, it provides people with the information they really need. As a kind of massive information, spatial information is applied to every aspect of human life. Whether it is the push of news on the Internet, the precise delivery of advertisements in e-commerce, the analysis of information in navigation and traffic conditions, the rescue of governments after disasters, or the location of buildings in the development of cities and enterprises, Can not do without big data support and analysis of geospatial information. Therefore, the analysis and processing of spatial relations in massive data is particularly important. The research of spatial relations has been more than 20 years. Spatial relations have been widely used in spatial reasoning and spatial analysis. Many scholars at home and abroad have made a lot of research results. Most of the existing qualitative spatial relationship models are based on sound algebraic systems, using prior knowledge to model artificial, the existing simple spatial relationship model can not fully express the actual application of the spatial relationship; The existing complex spatial relationship model can only be used to identify the specific complex spatial relationship, and it is not universal. Using the method based on the sound algebraic system to construct the spatial relation model artificially in big data, it is necessary to analyze the prior knowledge of the spatial object, and there are many elements to consider, which leads to the complexity of the formal representation system. It is even difficult to formalize expression, which can not meet the need of spatial expression in big data era. In order to solve the above problems in the existing spatial relational models, we study the spatial relational reasoning methods. In this paper, we propose a method to combine qualitative spatial reasoning with machine learning to generate spatial relational models. The method in this paper can generate spatial relation model without prior knowledge, and it has good generality. The main contents of this paper are as follows: (1) in this paper, the background, significance and development of spatial relations in spatial reasoning are discussed. Then it introduces the research content and the organization structure of this paper. (2) it introduces the basic concept of spatial relation and the related knowledge of machine learning, and describes the MLNB algorithm. (3) through the analysis of the problems existing in the existing spatial relationship model, A method of generating spatial relation model is presented, and the flow of this method is introduced. In this paper, the general feature set of spatial relations is proposed, and the domain decomposition, subregion and extended region partition of spatial objects are introduced in detail. On this basis, a feature classification strategy is proposed to group and combine sub-regions in order to reduce the number of features processed. (4) the method of generating spatial relation model proposed in this paper is applied to multiple data sets. The advantages of this method are analyzed by comparing the results applied to different data sets. The method is applied to the spatial relationship recognition of Chinese text, and good results are obtained. (5) the contents of this paper are summarized, and the prospect of further work is put forward.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP181;TP391.1

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本文編號(hào)：2151685