本文關鍵詞： 動態(tài)分布數(shù)據(jù) 知識發(fā)現(xiàn) 分布式推理 命名數(shù)據(jù)網(wǎng)絡 語義整合　出處：《湖南科技大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著網(wǎng)絡和通信技術的發(fā)展，互聯(lián)網(wǎng)絡已經(jīng)演變成為如今面向內(nèi)容分發(fā)與服務提供的普適信息基礎設施。而在大規(guī)模分布和動態(tài)的數(shù)據(jù)源中智能地發(fā)現(xiàn)知識和規(guī)則等是一個極具挑戰(zhàn)的問題。面向數(shù)據(jù)（data-oriented）、以內(nèi)容或信息為中心的網(wǎng)絡協(xié)議體系實現(xiàn)了從“機器互聯(lián)”到“信息互聯(lián)”的轉(zhuǎn)變。本研究面向命名數(shù)據(jù)網(wǎng)絡（NamedDataNetworking）體系結構，針對龐大的位置無關數(shù)據(jù)命名空間，，主要研究分布、自組織的語義推理方法、算法模型和系統(tǒng)實現(xiàn)方法。其中包括： 第一，研究適用命名數(shù)據(jù)網(wǎng)絡環(huán)境的、基于用戶驅(qū)動的語義推理機制。命名數(shù)據(jù)網(wǎng)絡是將內(nèi)容的名字與其位置相分離，以實現(xiàn)動態(tài)分布的網(wǎng)絡環(huán)境下基于內(nèi)容名字的路由。（1）在動態(tài)分布的網(wǎng)絡環(huán)境下，為了發(fā)現(xiàn)所有與用戶需求相關的、潛在的數(shù)據(jù)源，研究用戶推理請求的語義轉(zhuǎn)發(fā)過程，在轉(zhuǎn)發(fā)節(jié)點中設計具有推理能力的轉(zhuǎn)發(fā)引擎；（2）針對分布的推理結果的返回和匯聚過程，研究推理結果的語義整合和集聚機制及實現(xiàn)方法；（3）由于整個推理過程需要在分布、自組織的環(huán)境下進行，因此本文還研究提出具有動態(tài)自適應性和高效的推理機制和算法。 第二，研究基于請求驅(qū)動和路由過程中的推理規(guī)則。在動態(tài)分布的網(wǎng)絡環(huán)境下進行語義推理需要簡單、有效。首先，針對分布環(huán)境下用戶的推理需求，提出基于請求驅(qū)動的推理規(guī)則表示方法；然后，基于層次的內(nèi)容命名機制和路由索引的特征，提出請求的轉(zhuǎn)發(fā)推理機制，以發(fā)現(xiàn)所有潛在的知識源并將請求轉(zhuǎn)發(fā)給它們；最后，在匯聚點基于聚集的返回結果，研究匯聚結果的語義整合規(guī)則，以達到精練、整合推理結果的目標，并研究推理請求的演化規(guī)則，以根據(jù)已獲取的部分知識更新請求，從而發(fā)現(xiàn)用戶需要的更多潛在知識。 第三，研究基于命名數(shù)據(jù)網(wǎng)絡的、動態(tài)分布的語義轉(zhuǎn)發(fā)和整合推理的系統(tǒng)實現(xiàn)方法。在動態(tài)、分布的網(wǎng)絡環(huán)境下，分布式的語義轉(zhuǎn)發(fā)與語義整合推理需要多個匯聚節(jié)點的協(xié)作和配合。首先，本文在實際網(wǎng)絡中，模擬多個轉(zhuǎn)發(fā)節(jié)點和數(shù)據(jù)源節(jié)點，接下來實現(xiàn)自組織語義轉(zhuǎn)發(fā)和結果整合；最后，對系統(tǒng)的轉(zhuǎn)發(fā)和整合的效率進行了評價，并與當前的研究系統(tǒng)進行了橫向比較。 本論文的研究將解決網(wǎng)絡環(huán)境下動態(tài)分布推理和知識發(fā)現(xiàn)對位置的依賴性、知識發(fā)現(xiàn)請求與響應的時效性和準確性、內(nèi)容傳輸?shù)挠行缘戎匾獑栴}。在本論文提出的技術和算法的基礎上，我們提出了基于命名數(shù)據(jù)網(wǎng)絡的分布式推理框架、實現(xiàn)流程和實現(xiàn)模型，解決了動態(tài)分布知識的智能發(fā)現(xiàn)和整合的問題，為網(wǎng)絡環(huán)境下大規(guī)模知識的發(fā)現(xiàn)、使用和管理提供了較好的理論和技術基礎。
[Abstract]:With the development of network and communication technology, The Internet has evolved into a pervasive information infrastructure for content distribution and service delivery. Finding knowledge and rules intelligently in large-scale distributed and dynamic data sources is a challenging issue. Data-oriented, content-or information-centric network protocol architecture has transformed from "machine interconnection" to "information interconnection". In view of the huge position independent data namespace, this paper mainly studies the distribution, the self-organizing semantic reasoning method, the algorithm model and the system implementation method. First, a user-driven semantic reasoning mechanism suitable for naming data network environment is studied. Naming data network is to separate the name of content from its location. In the dynamically distributed network environment, in order to discover all the potential data sources related to the user's needs, the semantic forwarding process of user inference requests is studied. A forwarding engine with reasoning ability is designed in forwarding node. According to the return and convergence process of distributed reasoning results, the semantic integration and aggregation mechanism and implementation method of reasoning results are studied. (3) the whole reasoning process needs to be distributed. Therefore, a dynamic adaptive and efficient reasoning mechanism and algorithm are also proposed in this paper. Secondly, the reasoning rules based on request-driven and routing process are studied. Semantic reasoning needs to be simple and effective in the dynamic distributed network environment. Then, based on the characteristics of hierarchical content naming mechanism and routing index, a request forwarding reasoning mechanism is proposed to discover all potential knowledge sources and forward requests to them. Based on the aggregate return results at the convergence point, the semantic integration rules of the aggregation results are studied to achieve the goal of refining and integrating the inference results, and the evolution rules of the inference requests are studied to update the requests according to some acquired knowledge. To discover the user needs more potential knowledge. Thirdly, the system implementation method of dynamically distributed semantic forwarding and integrated reasoning based on named data network is studied. Distributed semantic forwarding and semantic integration reasoning need the cooperation and cooperation of multiple convergent nodes. Firstly, this paper simulates multiple forwarding nodes and data source nodes in the actual network, and then realizes self-organizing semantic forwarding and result integration. Finally, the efficiency of system forwarding and integration is evaluated and compared horizontally with the current research system. The research in this paper will solve the dynamic distributed reasoning and knowledge discovery dependence on the location, the timeliness and accuracy of the request and response of knowledge discovery. Based on the techniques and algorithms proposed in this paper, we propose a distributed reasoning framework based on named data network, implementation process and implementation model. It solves the problem of intelligent discovery and integration of dynamically distributed knowledge, and provides a good theoretical and technical basis for the discovery, use and management of large-scale knowledge in the network environment.
【學位授予單位】：湖南科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.1;TP393.09

【參考文獻】

相關期刊論文 前6條

1 倪燕;;基于泛在知識環(huán)境下的移動圖書館服務研究[J];合肥工業(yè)大學學報(社會科學版);2012年03期

2 石蓮;孫吉貴;;描述邏輯綜述[J];計算機科學;2006年01期

3 史忠植;常亮;;基于動態(tài)描述邏輯的語義Web服務推理[J];計算機學報;2008年09期

4 丁劍飛;何玉林;李成武;;基于本體的分布式實例推理技術研究[J];計算機仿真;2008年02期

5 楊力;從RDF,DAML+OIL到OWL——Ontology語言比較[J];農(nóng)業(yè)圖書情報學刊;2005年11期

6 熊明;;一階邏輯的內(nèi)涵語義[J];湖南科技大學學報(社會科學版);2006年06期

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