【摘要】：物聯(lián)網(wǎng)(Internet of Things,IoT)為了將傳感器、控制器、使用者等一系列的物進(jìn)行聯(lián)系,這就需要一個(gè)標(biāo)準(zhǔn)的通信協(xié)議提供支持。通過(guò)物之間的聯(lián)系,實(shí)現(xiàn)遠(yuǎn)程的管理控制以及智能化。在物聯(lián)網(wǎng)中配置了大量的傳感器,但這些傳感器產(chǎn)生的數(shù)據(jù)多種多樣且存在資源異構(gòu),將物聯(lián)網(wǎng)中同一物體進(jìn)行上傳時(shí),有可能會(huì)得到多種形式的表達(dá)。為解決機(jī)器不理解物的信息這一問(wèn)題,在物聯(lián)網(wǎng)中引入語(yǔ)義技術(shù),形成語(yǔ)義物聯(lián)網(wǎng)(Semantic Web of Things,SWoT)。針對(duì)以上不能正確地表達(dá)資源語(yǔ)義的問(wèn)題,在語(yǔ)義物聯(lián)網(wǎng)背景下,本文利用了本體和鏈接開(kāi)放數(shù)據(jù)來(lái)表達(dá)語(yǔ)義信息,提出了一種本體標(biāo)注和命名實(shí)體結(jié)合的傳感器語(yǔ)義強(qiáng)化方法(Ontology Annotation and Named Entity combined Sensor Semantic Enhancement Method,OANESSEM)。該方法使用 SSN 本體標(biāo)注傳感器,為傳感器數(shù)據(jù)添加語(yǔ)義,生成語(yǔ)義傳感器數(shù)據(jù),使機(jī)器理解傳感器的語(yǔ)義;并采用命名實(shí)體相關(guān)方法,使用鏈接開(kāi)放數(shù)據(jù)中的實(shí)體對(duì)傳感器數(shù)據(jù)進(jìn)行語(yǔ)義強(qiáng)化。在語(yǔ)義強(qiáng)化過(guò)程中,首先要從語(yǔ)義傳感器數(shù)據(jù)中抽取命名實(shí)體,然后通過(guò)知識(shí)庫(kù)DBpedia對(duì)其構(gòu)建命名實(shí)體候選集,最后使用基于圖的命名實(shí)體消歧方法,計(jì)算目標(biāo)實(shí)體的最優(yōu)解。語(yǔ)義強(qiáng)化的結(jié)果是從DBpedia中獲取對(duì)命名實(shí)體描述最準(zhǔn)確的目標(biāo)實(shí)體URI,用于描述傳感器,使用戶更好的地理解傳感器數(shù)據(jù)。最后依據(jù)該方法設(shè)計(jì)并開(kāi)發(fā)了本體標(biāo)注和命名實(shí)體結(jié)合的傳感器語(yǔ)義強(qiáng)化系統(tǒng)。首先設(shè)計(jì)了系統(tǒng)的總體結(jié)構(gòu),并給出系統(tǒng)的第一級(jí)數(shù)據(jù)流圖和各模塊數(shù)據(jù)流圖;然后給出系統(tǒng)運(yùn)行過(guò)程圖,并將實(shí)驗(yàn)結(jié)果與其它現(xiàn)有的方法進(jìn)行比較。實(shí)驗(yàn)結(jié)果表明,本文所構(gòu)建的語(yǔ)義強(qiáng)化系統(tǒng)可以較好地對(duì)傳感器的語(yǔ)義進(jìn)行描述,達(dá)到語(yǔ)義強(qiáng)化的目的,并且提高了查準(zhǔn)率、查全率,可以較好地實(shí)現(xiàn)傳感器語(yǔ)義的強(qiáng)化功能。
[Abstract]:Internet of things (Internet of Things,IoT) in order to connect sensors, controllers, users and so on, a standard communication protocol is needed. Through the connection between objects, the remote management and control and intelligence are realized. A large number of sensors are deployed in the Internet of things, but these sensors produce a variety of data and have heterogeneous resources. When uploading the same object in the Internet of things, it may be expressed in a variety of forms. In order to solve the problem that machines do not understand the information of objects, semantic technology is introduced into the Internet of things to form the semantic Internet of things (Semantic Web of Things,SWoT). In the context of semantic Internet of things, this paper uses ontology and link open data to express semantic information. In this paper, a new method of sensor semantic enhancement (Ontology Annotation and Named Entity combined Sensor Semantic Enhancement Method,OANESSEM) is proposed, which combines ontology tagging with named entities. In this method, SSN ontology is used to label the sensor, add semantics to the sensor data, generate the semantic sensor data, and make the machine understand the semantics of the sensor. Using the method of named entity correlation, the entities in the linked open data are used to enhance the semantic of sensor data. In the process of semantic enhancement, named entities are extracted from semantic sensor data first, then named entity candidate sets are constructed by knowledge base DBpedia. Finally, the optimal solution of target entities is calculated by using graph-based named entity disambiguation method. The result of semantic enhancement is that the target entity URI, which is the most accurate description of named entity, is obtained from DBpedia to describe the sensor, so that the user can understand the sensor data better. Finally, a sensor semantic enhancement system based on ontology annotation and named entity is designed and developed. First, the overall structure of the system is designed, and the first stage data flow diagram and each module data flow diagram of the system are given, then the running process diagram of the system is given, and the experimental results are compared with other existing methods. The experimental results show that the semantic enhancement system constructed in this paper can better describe the semantic of the sensor, achieve the purpose of semantic enhancement, and improve the precision and recall rate, which can better realize the semantic enhancement function of the sensor.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212;TP391.44;TN929.5

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本文編號(hào)：2391727