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.     

基于语义相似度的Web服务匹配研究

现有的语义Web服务匹配算法没有考虑到本体概念间的多元关系,导致概念的语义不能被完整地反映出来,从而影响了算法的匹配性能。利用本体概念间的多元关系定义了一种语义距离,并通过该语义距离给出了概念间的语义相似度计算方法,在此基础上提出基于语义相似度的Web服务匹配算法。该算法通过本体概念间的语义相似度来反映Web服务的匹配程度。最后,通过对比实验验证了该算法的可行性和有效性。     

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.     

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.     

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.

新闻视频结构化浏览与标注系统

阐述一种新颖的新闻视频结构化浏览和标注系统。应用基于时空切片分析的新闻主播检测方法和基于颜色直方图的镜头分割方法实现新闻视频的结构化。通过自动语音识别技术和特定语义概念模型的建立实现了对主播场景的文本信息标注和对新闻故事镜头的语义概念标注。该系统有利于用户根据个人爱好进行新闻视频的浏览和编辑,有效实现新闻视频的索引和浏览。     

基于XML的DNA公共数据模型

针对目前DNA数据组织与处理中存在的数据异构问题,提出一个基于XML的DNA公共数据模型（DCDM）。该模型具有很强的可扩展性,能克服一般公共数据模型的作用范围小的缺点,可用于构建DNA研究领域统一的DNA数据描述模式。实验结果表明,该模型能解决DNA数据异构中的语义异构。     

基于兴趣和信任评价的P2P语义社区模型

针对大规模网络中P2P信息共享面临的资源定位问题和“搭便车”现象,在考虑节点语义相似度、信任度和活跃强度的基础上,提出一种语义社区构建方法,提高P2P资源组织的结构性和定位准确性。建立基于信任评价的语义社区动态调整机制,以减少或消除“搭便车”的节点。     

一种支持QoS约束的语义Web服务发现框架

针对当前语义Web服务发现缺乏QoS约束的问题,构建一个支持QoS约束的语义Web服务发现框架。使用QoS认证中心确保QoS信息的动态更新。基于该框架提出一种支持QoS约束的语义Web服务匹配算法,通过实验证明该框架的有效性和可用性。     

基于P2P的语义Web服务发现机制

分析集中式语义Web服务发现机制存在的问题,利用P2P和本体技术,提出一种基于P2P的语义Web服务发现机制。将服务的部署与发布无缝地绑定在一起,按应用领域和服务目标将整个P2P网络组织成3层结构,基于该结构建立3阶段服务发现算法,达到优化网络结构、提高服务发现效率的目的。