动态文本流中的话题线索检测算法

针对动态文本流中的话题线索检测问题,提出一种基于语义相关度模型的话题线索检测算法。利用改进的语义相关度模型计算消息文本与话题线索之间的语义相关度,将消息划分至相应的话题线索。在真实数据集上的实验结果表明,与现有算法相比,该算法的F1测度值较高。     

基于词项扩展的XML信息检索反馈技术

为提高XML信息检索的查询准确率,提出一种基于词项扩展的XML信息检索反馈技术.利用词项所在节点的语义权重、词项与查询词间的相邻频度、共现程度,评估词项权重并排序,取权重较大的词项对初始检索词进行扩展,给出各因子的计算方法.在Wikipedia2009数据集上的实验结果表明,扩展后的查询准确率较高.     

基于图论的语义Web服务聚类方法

提出一种基于图论的聚类方法,用于在语义Web服务类别数量未知的情况下实现领域服务分类。通过计算待分类服务的相似度矩阵,得到相似度阈值,将相似度矩阵中超过该阈值的元素置为1,其余元素置为0,由此得到服务连接矩阵,再以该矩阵为图,逐个提取其中的最大完全子图,每个子图的节点服务就是一个服务类。理论分析与实验结果证明,该方法可以通过一次聚类得到服务的自然分群,聚类时间较短。     

A two-stage feature selection method for text categorization

Feature selection for text categorization is a well-studied problem and its goal is to improve the effectiveness of categorization, or the efficiency of computation, or both. The system of text categorization based on traditional term-matching is used to represent the vector space model as a document; however, it needs a high dimensional space to represent the document, and does not take into account the semantic relationship between terms, which leads to a poor categorization accuracy. The latent semantic indexing method can overcome this problem by using statistically derived conceptual indices to replace the individual terms. With the purpose of improving the accuracy and efficiency of categorization, in this paper we propose a two-stage feature selection method. Firstly, we apply a novel feature selection method to reduce the dimension of terms; and then we construct a new semantic space, between terms, based on the latent semantic indexing method. Through some applications involving the spam database categorization, we find that our two-stage feature selection method performs better.     

概念空间中上下位关系的意义识别研究

针对上下位关系在分类层级结构建立阶段遇到的多义性问题,给出一种概念空间中上下位关系意义识别的方法.单个概念的意义识别问题被转换为概念空间中上下位关系的意义识别.首先利用并列语境解决语境稀疏问题,获取上下位关系意义的语境.然后利用<同义词词林>对每个语境进行词义修正,以三种特征计算特征词权重,构建"关系一词'的高维向量空间,然后通过潜在语义分析降维,获取上下位关系意义的潜在语义,最后组平均聚类后得到关系的意义划分.在实验中,给出了聚类阈值自动调整函数,分析了词林和潜在语义分析的作用,实验结果证实了方法的有效性.     

一体化承载网络体系架构研究

传统信息网络因面向业务支撑设计而造成体系架构的刚性,无法满足业务发展多样性和网络异构的需求,因此无法适应下一代网络融合、可扩展等特点.面向服务提供的技术体系可以解决这一问题.文中以网络承载服务为核心,以可重构路由交换平台为关键支撑技术,设计出一种面向服务提供的网络体系架构--一体化承载网络(UCN)模型,以及基于UCN模型设计出的逻辑承载网的资源均衡构建算法(RBCA)并进行了网络仿真及结果分析,为下一代网络的研究和设计提供了新的思路.     

Axiom-based ontology matching

Abstract: Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model, where the projection (i.e. the main operator for reasoning with conceptual graphs which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs. We also introduce an ontology of representation, called MetaOCGL, dedicated to the reasoning of heavyweight ontologies at the meta-level.     

On semantic,pragmatic, and dialectic triangles

A model is built of semantic triangle characterized by its universal nature: it is suitable not only for the elements of the real world but also for any elements of the Universe. Various possibilities of the development of the given model are shown, including a dialectic triangle.     

Genetic algorithm for text clustering based on latent semantic indexing

In this paper, we develop a genetic algorithm method based on a latent semantic model (GAL) for text clustering. The main difficulty in the application of genetic algorithms (GAs) for document clustering is thousands or even tens of thousands of dimensions in feature space which is typical for textual data. Because the most straightforward and popular approach represents texts with the vector space model (VSM), that is, each unique term in the vocabulary represents one dimension. Latent semantic indexing (LSI) is a successful technology in information retrieval which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. Meanwhile, LSI takes into account the effects of synonymy and polysemy, which constructs a semantic structure in textual data. GA belongs to search techniques that can efficiently evolve the optimal solution in the reduced space. We propose a variable string length genetic algorithm which has been exploited for automatically evolving the proper number of clusters as well as providing near optimal data set clustering. GA can be used in conjunction with the reduced latent semantic structure and improve clustering efficiency and accuracy. The superiority of GAL approach over conventional GA applied in VSM model is demonstrated by providing good Reuter document clustering results.     

alt.metadata.health: Ontological Context for Data Use and Integration

Increasingly powerful computers and increased emphasis on evidence based decision making are creating a demand for merging and integrating data from different sources into a single data set. The demand for data is outstripping our ability to ensure data integrity, and sometimes analysis is performed on data that are not appropriate for the purposes they are used for. Here we describe problems that arise when data from different sources are merged, and we suggest that one way to add context to data so that users can make informed decisions about their ontological context is through ontology-based metadata. Examples of the problem are taken from health data with emphasis on difficulties in standardizing Emergency Room wait times. We describe eight fields that can be used to capture contextual metadata. These fields are captured using ethnographic methods from users and database stewards who frequently understand precisely how context and institutional usage have shaped interpretation of semantic fields. We argue that attaching a portable archive of ontological context to travel with data—based on information from users and developers—is a means of ensuring that data are integrated and compared in multiple contexts with greater integrity and more robust results.