本文選題：DL-Lite　切入點：本體調(diào)試　出處：《東南大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：互聯(lián)網(wǎng)的高速發(fā)展帶來了海量的數(shù)據(jù),其中大量的非結(jié)構(gòu)和半結(jié)構(gòu)的數(shù)據(jù)不利于自動化處理。語義Web為計算機(jī)能夠理解的結(jié)構(gòu)化數(shù)據(jù)提供了技術(shù)支持,促進(jìn)了人機(jī)協(xié)同工作。作為語義Web技術(shù)的重要組成部分,基于描述邏輯的本體語言O(shè)WL為其提供了嚴(yán)謹(jǐn)?shù)倪壿嫽A(chǔ)。在描述邏輯的語言體系中,DL-Lite能提供多項式時間內(nèi)的推理服務(wù),同時又具備UML以及E-R模型的表達(dá)能力。DL-lite下的應(yīng)用受到了學(xué)術(shù)界以及工業(yè)界的廣泛關(guān)注。在實際應(yīng)用中,本體的構(gòu)建與維護(hù)是一項復(fù)雜的過程。此過程常導(dǎo)致本體出現(xiàn)不一致性問題,從而使得標(biāo)準(zhǔn)推理服務(wù)失效。因此本體中的不一致性是本體工程中必須要處理的一類問題。針對本體中的不一致性問題,通常有兩種處理方法,一是找出并消除本體中的不一致：二是應(yīng)用非標(biāo)準(zhǔn)的推理方法,在不一致存在的情況下實現(xiàn)有意義的推理。本文重點關(guān)注DL-Lite中的不一致性問題并且結(jié)合以上兩種方法開展工作。具體內(nèi)容如下：(1)在本體調(diào)試方面,提出了一種基于圖的本體調(diào)試方法。將DL-Lite本體轉(zhuǎn)換成有向圖,通過路徑遍歷的方式來計算本體中的最小不可協(xié)調(diào)保持子集。基于圖的調(diào)試方法擺脫了對描述邏輯推理機(jī)的依賴,對比實驗結(jié)果表明新的方法有更高的執(zhí)行效率和穩(wěn)定性。(2)在本體修正方面,提出了一種基于圖的修正方法。新的修正方法首先通過修正狀態(tài)將不一致木體劃分成保留部分和待判別部分。在修正狀態(tài)的基礎(chǔ)上定義出一種新的滿足最小改變原則的修正算子。本文采用兩種方式實現(xiàn)了新的修正算子,一種是基于評分函數(shù)的,另一種基于碰集樹的方法。從對比實驗結(jié)果來看,基于評分函數(shù)的修正方法執(zhí)行效率要高于基于碰集樹的修正方法。(3)在不一致容忍語義方面,定義了兩種新的不一致容忍語義。新定義的不一致容忍語義與經(jīng)典的不一致容忍語義相比避免了計算整個ABox關(guān)于TBox的閉包,同時保證了原語義的表達(dá)能力,有效地提高了不一致容忍下推理服務(wù)的效率。(4)在不一致查詢應(yīng)答方面,提出了一種基于不一致容忍語義的查詢方法,新的查詢方法將本體和查詢分別以不同的規(guī)則轉(zhuǎn)換成圖,這避免了經(jīng)典查詢重寫方法導(dǎo)致的查詢項冗長的問題,并且優(yōu)化了查詢的效率。
[Abstract]:The rapid development of the Internet has brought a great deal of data, among which a large number of unstructured and semi-structured data are not conducive to automated processing. Semantic Web provides technical support for structured data that can be understood by computers. As an important part of semantic Web technology, OWL, an ontology language based on description logic, provides a rigorous logic basis for it. DL-Lite can provide reasoning services in polynomial time in the language system of description logic. At the same time, the application of UML and E-R model. DL-lite has been widely concerned by academia and industry. The construction and maintenance of ontology is a complex process, which often leads to inconsistency of ontology. Therefore, inconsistency in ontology is a kind of problem that must be dealt with in ontology engineering. One is to identify and eliminate inconsistencies in ontology, and the other is to apply non-standard reasoning methods. This paper focuses on the inconsistency in DL-Lite and combines the above two methods to carry out the work. The content of this paper is as follows: 1) in ontology debugging, In this paper, a graph-based ontology debugging method is proposed. The DL-Lite ontology is transformed into a directed graph, and the minimal uncoordinated holding subset in the ontology is calculated by path traversal. The graph-based debugging method gets rid of the dependence on the description logic inference engine. The experimental results show that the new method has higher execution efficiency and stability. In this paper, a graph based correction method is proposed. Firstly, the inconsistent wood body is divided into reserved and discriminant parts by modifying the state. Based on the modified state, a new method is defined to satisfy the minimum change. In this paper, a new modified operator is implemented in two ways. One is based on scoring function, the other is based on collision set tree. From the results of comparative experiments, the efficiency of the modified method based on score function is higher than that based on collision set tree. Two new inconsistency tolerance semantics are defined. Compared with the classical inconsistency tolerance semantics, the new inconsistency tolerance semantics avoids the computation of the entire ABox closure on the TBox, and at the same time ensures the expressive ability of the original semantics. The efficiency of reasoning service under inconsistent tolerance is improved effectively. In the aspect of inconsistent query response, a query method based on inconsistency tolerance semantics is proposed. The new query method transforms ontology and query into graphs with different rules, respectively. This avoids the problem of query term verbosity caused by classical query rewriting method and optimizes query efficiency.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.1

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本文編號：1612156