基于一阶逻辑的RDF模型的研究

XML为互联网应用提供了语法互操作性统一标准,而资源描述框架RDF定义了支持语义互操作的框架模型。作为RDF数据模型的类型系统,资源描述框架模式RDFS定义了一套扩充新的建模原语及其语义约束的机制。由于整个互联网语义化过程都以RDF模型为底层的模型支持,RDF数据模型及其类型系统的形式化程度直接影响和制约着更高层次上的语言和模型的形式化能力和推理能力。文章首先对RDFS类型系统做了非形式化分析,然后基于一阶逻辑定义了一套RDFS类型系统中对应类层次模型、类-实例模型和核心概念约束模型的事实-规则集。     

基于关系数据库的RDFS存储研究

RDF是表达和处理Web上元数据的标准,而RDFS是RDF的词汇描述语言。在介绍RDF（S）模型的基础上,对目前RDFS的几种存储方式进行了比较分析,提出了一种基于关系数据库的RDFS存储策略,并设计出了对应的关系模型,最后通过使用Jena解析实现了RDFS在关系数据库中的存储。     

RDB中基于RDFS的语义本体存储技术研究

资源描述框架(RDF)/资源描述框架模式(RDFS)是语义互联网架构中的技术。尽管XML/RDF数据库正迅速的发展,关系型数据库仍是目前企业级应用的首选。针对如何有效地把RDFS数据存储在关系数据库中,充分利用现有的数据库资源来管理RDFS数据,提出了一种RDB中基于RDFS的语义本体存储技术,实现了RDFS本体抽取,提出了RDFS本体的存储优化方案,提高了数据存储访问效率。     

基于RDFS的数据结构描述

资源描述框架(RDF)/资源描述框架模式(RDFS)是语义互联网架构中的技术。尽管XML/RDF数据库正迅速地发展,关系型数据库仍是目前企业级应用的首选。针对在应用中混合使用关系型数据库管理系统和RDF/RDFS的问题,该文提出了关系数据库表结构的RDFS定义方法以及在此基础上的数据获取方式,目的是在关系数据库与应用系统之间建立基于RDFS的抽象数据结构描述,实现数据结构的可配置性以及RDF数据的可访问性。     

构建XML本体信息研究

文中在简要介绍语义网概念的前提下,针对语义网模型中定义语义的RDFS层和本体层进行研究。在介绍RDF和RDFS的基本特性和一般形式的基础上,重点研究使用OWL构建XML本体信息的方法,并分析这些方法各自的特点和优势,文章将RDFS和OWL进行比较,阐明了它们之间的相互关系。所进行的研究能够对语义网络的本体构建的发展具有一定的促进作用。     

RDF(S)模型到关系数据库模式的转换

针对资源描述框架和资源描述框架模式(RDF(S))的数据管理问题,提出一种RDF(S)模型到关系数据库模式的转换方法。在对RDF(S)模型和关系数据库模式进行形式化定义的基础上,给出RDF(S)模型到关系数据库模式的转换规则和算法,设计并实现相应的转换系统,通过实验验证该转换方法的可行性和有效性。     

结合Rete的RDF数据分布式并行推理算法*

现有的资源描述框架(RDF)数据分布式并行推理算法大多需要启动多个MapReduce任务,但有些算法对于含有实例三元组前件的RDFS/OWL规则的推理效率低下,整体推理效率不高。针对此问题,文中提出结合Rete的RDF数据分布式并行推理算法(DRRM)。首先结合RDF数据本体,构建模式三元组列表和规则标记模型。在RDFS/OWL推理阶段,结合MapReduce实现Rete算法中的alpha阶段和beta阶段。然后对推理结果进行去重处理,完成一次RDFS/OWL全部规则推理。实验表明,文中算法能高效正确地实现大规模数据的并行推理。     

A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks

In real life, information about the world is uncertain and imprecise. The cause of this uncertainty is due to: deficiencies on given information, the fuzzy nature of our perception of events and objects, and on the limitations of the models we use to explain the world. The development of new methods for dealing with information with uncertainty is crucial for solving real life problems. In this paper three interval type-2 fuzzy neural network (IT2FNN) architectures are proposed, with hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate backpropagation). At the antecedents layer, a interval type-2 fuzzy neuron (IT2FN) model is used, and in case of the consequents layer an interval type-1 fuzzy neuron model (IT1FN), in order to fuzzify the rule’s antecedents and consequents of an interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). IT2-TSK-FIS is integrated in an adaptive neural network, in order to take advantage the best of both models. This provides a high order intuitive mechanism for representing imperfect information by means of use of fuzzy If-Then rules, in addition to handling uncertainty and imprecision. On the other hand, neural networks are highly adaptable, with learning and generalization capabilities. Experimental results are divided in two kinds: in the first one a non-linear identification problem for control systems is simulated, here a comparative analysis of learning architectures IT2FNN and ANFIS is done. For the second kind, a non-linear Mackey-Glass chaotic time series prediction problem with uncertainty sources is studied. Finally, IT2FNN proved to be more efficient mechanism for modeling real-world problems.     

Atlas: Storing, updating and querying RDF(S) data on top of DHTs

The RDF(S) data model has been proposed for encoding metadata about Web resources. As more and more Web resources are annotated using RDF(S), there is an urgent need for efficiently dealing with this large volume of data. In this paper, we present Atlas, a peer-to-peer system for storing, updating and querying RDF(S) data. The Atlas system has been built using the distributed hash table Bamboo. Atlas was developed in the context of project OntoGrid, where it was used as a distributed repository for RDF(S) metadata describing Grid services and resources. The development of Atlas continues in other projects that our group participates currently. This paper gives an overview of the most recent version of Atlas and discusses a representative application.     

基于结构元矿山产能分配模糊线性规划模型

在矿山产能分配中,矿产品的大约利润、生产所需大约资源及提供生产的大约资源均为模糊数,综合模糊数学与模糊线性规划特点,建立基于结构元素的矿山产能分配的模糊线性规划预测模型。运用模糊结构元理论,将模型中用三角模糊数表达的模糊数用结构元表示,使模型中的参数的隶属函数得到解析表达,进而利用结构元加权序理论,将模型转为经典线性规划模型,实现对实际问题中的模糊事件的精确表达;且约束条件数目相对较少,简化了原模型求解,进而得到某矿山产能取得最大可能利润时的分配,可为决策者提供更精确信息。     

Designing genetic regulatory networks using fuzzy Petri nets approach

In this paper, we have successfully presented a fuzzy Petri net (FPN) model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition (HFT) to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.     

模糊Jena的研究

用Jena[11]建立的本体中概念及其关系都是确定的,但是,现实世界中事物及其关系往往具有模糊性且这种模糊性会随着事物的发展而变化.为了将Web上资源及其关系的模糊性表示出来,在RDF[9]的三元组(主体、谓词、客体)中的谓词部分加入一个表示谓词的模糊性的权值,提出一个新的概念-模糊资源描述框架(FRDF,Fuzzy ...     

Some interesting properties of the fuzzy linguistic model based on discrete fuzzy numbers to manage hesitant fuzzy linguistic information

The management of hesitant fuzzy information is a topic of special interest in fuzzy decision making. In this paper, we focus on the use and properties of the fuzzy linguistic modelling based on discrete fuzzy numbers to manage hesitant fuzzy linguistic information. Among these properties, we can highlight the existence of aggregation functions with no need of transformations or the possibility of a greater flexibilization of the opinions of the experts, even using different linguistic chains (multigranularity). Furthermore, based on these properties we perform a comparison between this model and the one based on hesitant fuzzy linguistic term sets, showing the advantages of the former with respect to the latter. Finally, a fuzzy decision making model based on discrete fuzzy numbers is proposed.     

面向后件集的模糊推理机制: 用于区间型type-2模糊逻辑系统

目前,大多数模糊推理都是利用t-范数和t-余范数或其改进形式对连接词进行建模,这些模型不能将模糊规则中前件集与后件集之间的相关性信息引入到模糊推理过程,这会丢失蕴含在规则中的一些信息甚至导致推理结果与实际经验严重不符.为解决此问题,本文首先引入模糊集合面向对象变换的概念,并将其推广,建立了合成type-2模糊集合模型.基于此模型,针对区间型type-2模糊逻辑系统,提出一种面向后件集的模糊推理机制,该机制能将前件集与后件集的相关性信息(包括清晰数和模糊数两种情形)引入到模糊推理过程.仿真结果表明,该方法能捕获到模糊规则中更多的不确定性信息,并为模糊逻辑系统的设计提供更大的自由度.