本文關(guān)鍵詞： 語義物聯(lián)網(wǎng) 情景不一致性 本體 粗糙集 形式概念分析 證據(jù)論　出處：《大連海事大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在物聯(lián)網(wǎng)中,由于物的信息具有多樣化的描述形式且機(jī)器不能夠完全理解這些物的信息(物聯(lián)網(wǎng)的內(nèi)在矛盾),進(jìn)而很難有效地為普通用戶提供語義服務(wù)。語義物聯(lián)網(wǎng)能夠消解物聯(lián)網(wǎng)的內(nèi)在矛盾,為用戶提供相應(yīng)的語義服務(wù)。它在物聯(lián)網(wǎng)的基礎(chǔ)上納入"語義協(xié)同",即基于本體的語義標(biāo)注和基于本體的語義理解。情景感知是語義物聯(lián)網(wǎng)的核心組成部分,情景不一致性是情景感知的關(guān)鍵問題,導(dǎo)致用戶無法獲得正確的情景進(jìn)而無法進(jìn)行情景推理。本文綜合利用粗糙集理論、本體、形式概念分析以及證據(jù)論,提出一種適用于語義物聯(lián)網(wǎng)的情景不一致性消解策略,利用本體來標(biāo)注情景和情景模式并建立常識(shí)庫,找出不一致的情景,利用粗糙集理論從常識(shí)庫的決策表中獲得約簡屬性,再利用形式概念分析和證據(jù)論找出最佳的情景。通過與近期相關(guān)研究(基于語義的情景不一致性消除算法、基于約束的情景不一致性消除算法以及基于證據(jù)論的情景不一致性消除算法)的評(píng)估對(duì)比以及大量實(shí)驗(yàn)證明本文所提出的策略具有較高的正確率和可行性,本文所提出的方法既具有語義性,也適應(yīng)語義物聯(lián)網(wǎng)的動(dòng)態(tài)環(huán)境。
[Abstract]:In the Internet of things, Because the information of objects has a variety of description forms and the machine can not fully understand the information of these objects (the inherent contradiction of the Internet of things, it is very difficult to provide semantic services to ordinary users effectively. The semantic Internet of things can be dispelled. The inherent contradiction of the Internet of things, It includes semantic collaboration on the basis of the Internet of things, namely Ontology-based semantic annotation and Ontology-based semantic understanding. Situational perception is the core component of semantic Internet of things. Situational inconsistency is a key problem in situational perception, which results in the user being unable to obtain the correct situation and then unable to infer the situation.In this paper, rough set theory, ontology, formal conceptual analysis and evidence theory are synthetically used. In this paper, a scenario inconsistency resolution strategy suitable for semantic Internet of things is proposed. Ontology is used to annotate scenarios and situational patterns, and common sense database is established to find inconsistent scenarios, and rough set theory is used to obtain reduction attributes from decision tables of common sense database. Then we use formal conceptual analysis and evidence theory to find out the best scenario. The evaluation and comparison of Constraint-based scenario inconsistency cancellation algorithm and evidence-based scenario inconsistency cancellation algorithm and a large number of experiments show that the proposed strategy has a high accuracy and feasibility. The method proposed in this paper is both semantic and adaptive to the dynamic environment of semantic Internet of things.
【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)】：TP391.44;TN929.5

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本文編號(hào)：1510378