基于本体的上下文感知中间件框架

上下文感知是普适计算的核心技术之一,而描述和理解上下文信息是上下文感知的前提.由于上下文信息种类繁多、感知方式迥异,目前开发面向特定应用的上下文感知系统缺乏统一的机制和通用的架构,增加了系统开发的成本.引入语义Web技术,利用本体对上下文信息进行建模,采用本体描述语言描述上下文模型,提供了一个公共的上下文本体以实现多个独立开发的上下文感知系统对知识的共享和推理,构建了通用的上下文感知中间件框架,从而实现对域内上下文知识的共同理解.     

移动协作学习的上下文感知模型研究

根据移动协作学习的特点和个性化需求,提出了一种移动协作学习的上下文感知计算模型(M-CCLM),包括上下文信息获取、表达、优化和推理.基于上下文逻辑给出了上下文融合的优化算法和决策行动的贝叶斯推理算法.通过实例说明了模型的应用方法.     

基于本体的上下文感知中间件框架

上下文感知是普适计算的核心技术之一,而描述和理解上下文信息是上下文感知的前提。由于上下文信息种类繁多、感知方式迥异,目前开发面向特定应用的上下文感知系统缺乏统一的机制和通用的架构,增加了系统开发的成本。引入语义Web技术,利用本体对上下文信息进行建模,采用本体描述语言描述上下文模型,提供了一个公共的上下文本体以实现多个独立开发的上下文感知系统对知识的共享和推理,构建了通用的上下文感知中间件框架,从而实现对域内上下文知识的共同理解。     

模糊描述逻辑知识库查询蕴涵的判定方法

大规模领域本体的快速发展对语义Web领域的数据访问提出了更高的要求,而基本的本体推理服务已不能满足数据密集型应用中处理复杂查询(主要是合取查询)的迫切需要.为此,大量的研究工作集中在本体和描述逻辑知识库合取查询算法的设计实现上,并开发出了很多知识库存储和查询的实用工具.近来模糊本体和模糊描述逻辑的研究,特别是它们在处理语义Web中模糊信息方面,得到了广泛关注.文中重点研究了模糊SH这一族极富表达能力的描述逻辑知识库的合取查询问题,提出了相应的基于推演表的算法,证明了算法对于f-SHOIQ的真子逻辑的可靠性、完备性和可终止性.证明了算法对于f-SHOIQ是可靠的,并分析了导致算法不可终止的原因.对于该问题的数据复杂度,证明了当查询中不存在传递角色时其严格的CONP上限.对于联合复杂度,汪明了算法关于知识库和查询大小的CO3NEXPTIME时间复杂度上限.     

面向物联网的分布式上下文敏感复杂事件处理方法

物联网产生的数据具有大数据特征,而这些数据难以用现有数据处理技术进行有效处理.作为物联网中间件的核心技术,复杂事件处理技术具备大数据的海量、复杂性等特征和实时处理的需求.上下文敏感是复杂事件处理引擎的重要特征.提出一种高效的面向物联网的分布式上下文敏感复杂事件处理架构和方法.该方法使用模糊本体进行上下文建模,以支持事件的不确定性及模糊事件查询问题.以基于模糊本体的查询和基于相似性的分布式推理为基础,生成复杂事件查询规划,并通过查询重写,把上下文相关查询转换为上下文无关子查询.根据不同的事件模型和上下文划分数据,并通过优化和多级并行来提高性能.实验结果表明该方法能够处理模糊事件上下文,对于面向物联网的分布式上下文敏感复杂事件处理具有比一般方法更好的性能和可伸缩性.     

一种结合自编码器与强化学习的查询推荐方法

查询推荐的目的是发掘搜索引擎用户的查询意图,并给出相关查询推荐.传统的查询推荐方法主要依靠人工提取查询的相关特征,如查询频率、查询时间、用户点击次数和停留时间等,并使用统计学习算法或排序算法给出查询推荐.近年来,深度学习方法在查询推荐问题上获得了广泛应用.现有的用于查询推荐的深度学习方法大多是基于循环神经网络,通过对查询日志中所有查询的语义特征进行建模以预测用户的下一查询.但是,现有的深度学习方法生成的查询推荐上下文感知能力较差,难以准确捕捉用户查询意图,且未充分考虑时间因素对查询推荐的影响,缺乏时效性和多样性.针对上述问题,文中提出了一种结合自编码器与强化学习的查询推荐模型(Latent Variable Hierarchical Recurrent Encoder-Decoder with Time Informa-tion of Query and Reinforcement Learning,VHREDT-RL).VHREDT-RL引入了强化学习联合训练生成器和判别器,从而增强了生成查询推荐的上下文感知能力;利用融合查询时间信息的隐变量分层递归自编码器作为生成器,使得生成查询推荐有更好的时效性和多样性.AOL数据集上的实验结果表明,文中提出的VHREDT-RL模型获得了优于基准方法的精度、鲁棒性和稳定性.     

基于RDF的查询引擎系统架构

Iseek可建立在从关系数据库到专用RDF三元组存储的多种存储库上，为了实现准确查询，该查询引擎采用RQL作为查询语言，而且由于加入了基于描述逻辑的推理机制，它能为用户返回语义上相关度较高的信息。     

基于上下文感知和RFID的智能交互系统

上下文感知是在移动计算环境中实现人机自然交互的关键,该文分析在信息空间和物理空间融合环境中,上下文应具有的意义、特征和作用.结合RFID技术的特点和发展趋势,提出通过基于RFID与移动计算获取上下文以进行智能交互的思想.采用被动式RFID标签跟踪并存储上下文数据,以蓝牙作为无线交互信道,结合移动终端PDA实现嵌入式系统,通过实验给出应用效果.该原形系统在可穿戴计算、医疗看护、残障协助和路标导航等方面具有实验推广价值.     

支持上下文感知服务的中间件原型*

提出了基于传感器网络环境的上下文感知应用开发中间件原型的体系结构MidCASE,重点阐述了中间件的体系结构设计和软件运行时基于服务的分布式运行架构.讨论了上下文感知过程中的两个关键问题,包括上下文信息建立的方法和感知过程中上下文信息与高层通信的调度模型.最后,以该中间件原型为基础,通过在医疗护理的场景下实现上下文感知应用并证明了其易用性与支撑作用.     

Web服务组合上下文模型

基于上下文感知计算,提出一种新颖的Web服务组合上下文模型.一方面,服务组合中的信息被定义为服务和用户上下文,前者又被区分为概要、会话和填充上下文;另一方面,采用多策略机制管理上下文.为此,定义了一种基于ECA规则的语言-服务扩展策略描述语言(PDL-S).采用以上语言描述上下文管理策略和组合服务.该模型不但充分利用了上下文感知计算,而且具有良好的协作性、扩展性和灵活性.     

A Default Logic Based Framework for Context-Dependent Reasoning with Lexical Knowledge

Lexical knowledge is increasingly important in information systems—for example in indexing documents using keywords, or disambiguating words in a query to an information retrieval system, or a natural language interface. However, it is a difficult kind of knowledge to represent and reason with. Existing approaches to formalizing lexical knowledge have used languages with limited expressibility, such as those based on inheritance hierarchies, and in particular, they have not adequately addressed the context-dependent nature of lexical knowledge. Here we present a framework, based on default logic, called the dex framework, for capturing context-dependent reasoning with lexical knowledge. Default logic is a first-order logic offering a more expressive formalisation than inheritance hierarchies: (1) First-order formulae capturing lexical knowledge about words can be inferred; (2) Preferences over formulae can be based on specificity, reasoning about exceptions, or explicit priorities; (3) Information about contexts can be reasoned with as first-order formulae formulae; and (4) Information about contexts can be derived as default inferences. In the dex framework, a word for which lexical knowledge is sought is called a query word. The context for a query word is derived from further words, such as words in the same sentence as the query word. These further words are used with a form of decision tree called a context classification tree to identify which contexts hold for the query word. We show how we can use these contexts in default logic to identify lexical knowledge about the query word such as synonyms, antonyms, specializations, meronyms, and more sophisticated first-order semantic knowledge. We also show how we can use a standard machine learning algorithm to generate context classification trees.     

状态自动机支持的环境感知应用框架研究

环境感知是普及计算中一个重要的研究领域。为了便于开发环境感知应用程序,许多研究者已经提出了一些环境感知应用框架。然而,目前的一些环境感知应用框架侧重于让应用程序直接订阅或查询相关的环境信息,使得开发者需要关注众多的环境信息且要定义许多相似的规则来区分众多不同的操作,造成了开发和修改环境感知的应用程序仍存在着较多的困难。为解决这些问题,本文在研究环境感知应用框架的基础上,引入了信息空间环境状态的概念,提出了一种基于状态自动机的环境感知改进框架BS-CTK,并设计和实现了相应的组件以支持基于状态自动机的环境感知应用开发。     

面向语义信息查询的模糊本体模型

针对领域知识建模时的模糊性、不确定性与信息查询时的局限性,提出一种基于模糊控制规则的模糊本体模型。利用基于模糊聚类的本体机器学习方法构建模糊控制规则库,通过计算模糊相似矩阵得到模糊概念的语义关联,对词汇相关概念进行语义分析与扩展获取模糊概念间的本质语义关系,实现基于模糊概念属性值的信息查询与语义共用。实验结果表明,该模型在语义查询上有更完善的推理机制,能有效获取语义信息。     

Intelligent Fril/SQL interrogator

The intelligent Fril/SQL interrogator is an object‐oriented and knowledge‐based support query system, which is implemented by the set of logic objects linking one another. These logic objects integrate SQL query, support logic programming language—Fril and Fril query together by processing them in sequence in slots of each logic object. This approach therefore takes advantage of both object‐oriented system and a logic programming‐based system. Fuzzy logic data mining and a machine learning tool kit built in the intelligent interrogator can automatically provide a knowledge base or rules to assist a human to analyze huge data sets or create intelligent controllers. Alternatively, users can write or edit the knowledge base or rules according to their requirements, so that the intelligent interrogator is also a support logic programming environment where users can write and run various Fril programs through these logic objects. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 279–302, 2007.     

A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques

In this paper, we extend the work of Kraft et al. to present a new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. First, we present a fuzzy agglomerative hierarchical clustering algorithm for clustering documents and to get the document cluster centers of document clusters. Then, we present a method to construct fuzzy logic rules based on the document clusters and their document cluster centers. Finally, we apply the constructed fuzzy logic rules to modify the user's query for query expansion and to guide the information retrieval system to retrieve documents relevant to the user's request. The fuzzy logic rules can represent three kinds of fuzzy relationships (i.e., fuzzy positive association relationship, fuzzy specialization relationship and fuzzy generalization relationship) between index terms. The proposed fuzzy information retrieval method is more flexible and more intelligent than the existing methods due to the fact that it can expand users' queries for fuzzy information retrieval in a more effective manner.     

A Hybrid Modular Context-aware Services Adaptation for a Smart Living Room

Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in the realization of smart homes. The main objective of smart homes is the automation of tasks that might be complex or tedious for inhabitants by distracting them from concentrating on setting and configuring home appliances. Such automation could improve comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Context awareness is a key enabling feature for development of smart homes. It allows the automation task to be done proactively according to the inhabitant’s current context and in an unobtrusive and seamlessly manner. Although there are several works conducted for the development of smart homes with various technologies, in most cases, robust. However, the context-awareness aspect of services adaptation was not based on clear steps for context elements extraction (resp. clear definition of context). In this paper, we use the divide and conquer approach to master the complexity of automation task by proposing a hybrid modular system for context-aware services adaptation in a smart living room. We propose to use for the context-aware adaptation three techniques of machine learning, namely Naïve Bayes, fuzzy logic and case-based reasoning techniques according to their convenience.     

数据库中的模糊规则获取方法

分析了规则化的模糊逻辑原理。介绍了模糊化的规则获取和推理方法,由分析可以看出,基于模糊逻辑的规则获取和推理方法能在数据库中找出其规则,并且在实际系统中有着一定的意义。     

An efficient approach to an automatic detection of erythemato-squamous diseases

This paper presents a novel approach to automatic detection of the erythemato-squamous diseases based on fuzzy extreme learning machine (FELM). Enormous computational efforts are required to classify these erythemato-squamous diseases. Some of the approaches performed previously are through fuzzy logic, artificial neural networks and neuro-fuzzy models. FELM-based differential diagnosis of these diseases involves decisions made by fuzzy logic and extreme learning machine (ELM) with greater efficiency in both time and accuracy. In this paper, we develop a user-friendly interface and this tool will be useful for a dermatologist to estimate the six types of erythemato-squamous diseases with the help of patient’s histopathological and clinical data. Then, the developed interface is derived inbuilt using neural networks, adaptive neuro-fuzzy inference system and FELM. A dataset containing records of 366 patients with 34 features that define six disease characteristics was taken, of which 310 records were used as training data and 56 other records used as testing data. The dataset was preprocessed to obtain fuzzy values as input to get more accurate results in FELM. Given a training set of such records, ELM approach is applied. By combining fuzzy logic and ELM, more accurate results with increased performance are obtained with less computational efforts. Finally, the proposed FELM model proves to be a potential solution for the diagnosis of erythemato-squamous diseases with significant improvement in computational time and accuracy compared with other models discussed in the recent literature.     

面向情境感知的不确定性数据融合策略

情境感知计算是对传感器感知的数据进行融合从而得到用户情境信息的过程。文中针对传感器数据存在的不确定性问题,在Context Toolkit模型的基础上提出了一种面向情境感知的数据融合策略,把数据融合分为底层融合和高层融合,在这两层上分别采用模糊逻辑和动态贝叶斯网络的数据融合方法。仿真实验结果表明,这种将模糊逻辑和动态贝叶斯网络相结合的策略能有效地处理情境感知过程中的不确定数据和信息,从而提高情境感知计算的容错性和准确度。