本文選題：本體 + OWL��； 參考：《鄭州大學(xué)》2017年碩士論文

【摘要】：隨著語義網(wǎng)中數(shù)據(jù)的不斷豐富和語義服務(wù)的不斷發(fā)展,語義網(wǎng)中開始出現(xiàn)大量的不確定數(shù)據(jù),給語義網(wǎng)的應(yīng)用帶來很大挑戰(zhàn),不確定性數(shù)據(jù)的表示和推理變得更加重要。本體能夠?qū)φZ義網(wǎng)中的語義和知識進(jìn)行建模,但現(xiàn)有的本體語言無法直接表示不確定性知識,需要對本體進(jìn)行擴(kuò)展。目前擴(kuò)展方式主要基于概率和基于模糊兩方面,但現(xiàn)有的研究往往只關(guān)注于其中一方面,而在實際應(yīng)用中兩者可能同時出現(xiàn)。針對現(xiàn)狀,本文對不確定性的表示和推理的研究進(jìn)行分析和總結(jié),并對模糊概率知識的表示和推理進(jìn)行研究,提出基于模糊多實體貝葉斯網(wǎng)絡(luò)(模糊MEBN)的本體表示和推理的框架。本文主要工作如下:首先,對語義網(wǎng)中不確定性知識的表示方法進(jìn)行研究,將模糊MEBN理論和本體相結(jié)合,提出基于OWL2(Web Ontology Language)的模糊MEBN本體語言FuzzyPR-OWL。該本體語言通過OWL2語言構(gòu)建能表示模糊概率知識的本體類和屬性,提供對模糊語義和不確定關(guān)系的描述,并給出語法定義和語義解釋,同時用實例說明FuzzyPR-OWL本體構(gòu)建領(lǐng)域本體的方法。之后,對不確定性知識的推理方法進(jìn)行研究�；贔uzzyPR-OWL本體表示提出模糊概率本體的推理框架。論文結(jié)合模糊概率和貝葉斯網(wǎng)絡(luò)的信念傳播算法,在節(jié)點間傳播的消息中增加模糊規(guī)則的影響因素,提出基于模糊概率的信念傳播算法,在此基礎(chǔ)上給出推理過程,包括數(shù)據(jù)的模糊化、SSFBN的構(gòu)建以及模糊信念傳播。最后,通過實驗驗證模糊概率本體的表示和推理方法的可行性和有效性。先用所提出的方法對汽車防撞警告系統(tǒng)進(jìn)行建模和推理,把增加模糊狀態(tài)后的推導(dǎo)結(jié)果與無模糊的概率推導(dǎo)結(jié)果作對比,得出模糊狀態(tài)對目標(biāo)節(jié)點概率的影響結(jié)果,然后在數(shù)據(jù)集上利用十倍交叉驗證法對算法準(zhǔn)確性進(jìn)行評估。實驗結(jié)果表明,本文提出的本體語言FuzzyPR-OWL能夠有效表示和推理模糊概率知識,為不確定性信息的表示和推理提供一種新的解決方案。
[Abstract]:With the continuous enrichment of data and the development of semantic services in the semantic Web, a large number of uncertain data appear in the semantic Web, which brings great challenges to the application of the semantic Web, and the representation and reasoning of uncertain data becomes more important. Ontology can model semantics and knowledge in semantic web, but the existing ontology language can not express uncertain knowledge directly, so ontology needs to be extended. At present, the methods of expansion are mainly based on probability and fuzzy. However, the existing researches focus on only one of them, which may occur simultaneously in practical applications. In view of the present situation, this paper analyzes and summarizes the representation and reasoning of uncertainty, and studies the representation and reasoning of fuzzy probability knowledge. A framework for ontology representation and reasoning based on fuzzy multi-entity Bayesian network (fuzzy MEBN) is proposed. The main work of this paper is as follows: firstly, the representation method of uncertain knowledge in semantic web is studied. The fuzzy MEBN ontology language FuzzyPR-OWL based on OWL2 Web Ontology language is proposed by combining fuzzy MEBN theory with ontology. The ontology language constructs ontology classes and attributes that can represent fuzzy probability knowledge through owl 2 language, provides descriptions of fuzzy semantics and uncertain relations, and gives syntax definition and semantic interpretation. An example is given to illustrate the method of constructing domain ontology by FuzzyPR-OWL ontology. Then, the reasoning method of uncertain knowledge is studied. A fuzzy probability ontology reasoning framework is proposed based on fuzzy PR-OWL ontology representation. Based on fuzzy probability and belief propagation algorithm of Bayesian network, this paper proposes a belief propagation algorithm based on fuzzy probability by adding the influence factors of fuzzy rules to messages propagated between nodes, and then gives the reasoning process. It includes the fuzzification of data and the construction of SSFBN and the propagation of fuzzy belief. Finally, the feasibility and validity of the representation and reasoning method of fuzzy probability ontology are verified by experiments. First, the vehicle anti-collision warning system is modeled and inferred by the proposed method, and the results after adding fuzzy state are compared with the result of non-fuzzy probability derivation, and the effect of fuzzy state on the probability of target node is obtained. Then the accuracy of the algorithm is evaluated by using ten times cross validation method on the data set. Experimental results show that the proposed ontology language FuzzyPR-OWL can effectively represent and infer fuzzy probability knowledge and provide a new solution for the representation and reasoning of uncertain information.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.1;TP18

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本文編號：2015314