共查询到19条相似文献，搜索用时 177 毫秒
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将语义web的优势和网格技术的先进技术结合起来研究,提出知识管理的语义网格构架,探讨语义网格环境下知识管理模型建立的主要技术和语义网格环境下相关的知识服务,重点论述基于本体的语义注释技术和基于本体的知识发现以及在语义网格环境下隐性知识的转化模型,在此基础上描述语义网格环境下构建知识管理模型的思路,并从应用层、空间语义层、知识网格服务层和分布式资源四个层次对知识管理模型进行分析. 相似文献
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结合网格技术的发展将数字图书馆环境中数字资源的语义互联结合起来,提出了基于语义网格的数字图书馆的体系结构和知识组织模型,并探讨这种知识组织模型的关键技术与服务模式. 相似文献
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文章提出两种Web环境下的语义挖掘模型,分别是基于语义标注的Web语义挖掘模型,即在语义标注的基础上实现智能化知识挖掘过程,获取高质量知识模式;基于本体映射的Web语义挖掘模型,即参照领域本体概念体系,运用本体映射技术,对所获取的知识模式进行语义修正.通过对语义分类模式的预测准确率、模型创建速度的实验对比分析,基于本体映射的Web语义挖掘模型所提出的算法略占优势. 相似文献
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对语义网格及其在数字图书馆信息检索中应用的探讨 总被引：1，自引：0，他引：1
本文在对语义网及网格技术进行简要介绍、分析的基础上,提出了语义网格环境下数字图书馆信息检索模型,并详细阐述用户获取信息的过程。在文章的结尾,还就相关研究的发展方向进行展望。 相似文献
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语义网络和网格技术在数字图书馆建设中的重要性已被业界所关注。语义网络应用于数字图书馆建设可以对其资源进行基于语义的标注,提供基于语义的资源浏览与检索;网格技术应用于数字图书馆建设可以整合分布、异构、自治的数字资源,获得资源透明调用的能力。 相似文献
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我国数字图书馆网格研究综述 总被引：1，自引：0，他引：1
本文介绍了数字图书馆网格的概念、数字图书馆网格相关技术,包括嵌入式系统、普适计算、语义网格、知识网格。以及研究数字图书馆网格的研究领域。 相似文献
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《Information processing & management》2005,41(5):1277-1297
In order to organise and manage geospatial and georeferenced information on the Web making them convenient for searching and browsing, a digital portal known as G-Portal has been designed and implemented. Compared to other digital libraries, G-Portal is unique for several of its features. It maintains metadata resources in XML with flexible resource schemas. Logical groupings of metadata resources as projects and layers are possible to allow the entire metadata collection to be partitioned differently for users with different information needs. These metadata resources can be displayed in both the classification-based and map-based interfaces provided by G-Portal. G-Portal further incorporates both a query module and an annotation module for users to search metadata and to create additional knowledge for sharing respectively. G-Portal also includes a resource classification module that categorizes resources into one or more hierarchical category trees based on user-defined classification schemas. This paper gives an overview of the G-Portal design and implementation. The portal features will be illustrated using a collection of high school geography examination-related resources. 相似文献
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融合Logistic方程与Markov模型的开放政府用户参与行为分析 总被引：5，自引：1，他引：4
[目的/意义] 开放政府是新时代下支撑社会管理的重要元素,从用户参与行为的视角剖析开放政府的认同度、互动力以及传播力,对消除政府与用户间数字鸿沟、提高用户参与度具有重要研究意义。[方法/过程] 本文融合Logistic方程与Markov模型探索并预测了开放政府的用户参与行为随时间变化的趋势,并以"思想火炬"为样本官微实证了用户的参与度水平。[结果/结论] 研究表明：用户参与行为间具备相互作用关系,转发行为更易受到点赞行为与评论行为的影响;融合模型能够预测用户参与行为的发展脉络,进而指导官微进行实时预警;当信息资源聚焦国际关系时,公众参与度显著提升。 相似文献
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In this article, we investigate the use of a probabilistic model for unsupervised clustering in text collections. Unsupervised clustering has become a basic module for many intelligent text processing applications, such as information retrieval, text classification or information extraction. 相似文献
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Recently, sentiment classification has received considerable attention within the natural language processing research community. However, since most recent works regarding sentiment classification have been done in the English language, there are accordingly not enough sentiment resources in other languages. Manual construction of reliable sentiment resources is a very difficult and time-consuming task. Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language (typically English) for sentiment classification of text documents in another language. Most existing research works rely on automatic machine translation services to directly project information from one language to another. However, different term distribution between original and translated text documents and translation errors are two main problems faced in the case of using only machine translation. To overcome these problems, we propose a novel learning model based on active learning and semi-supervised co-training to incorporate unlabelled data from the target language into the learning process in a bi-view framework. This model attempts to enrich training data by adding the most confident automatically-labelled examples, as well as a few of the most informative manually-labelled examples from unlabelled data in an iterative process. Further, in this model, we consider the density of unlabelled data so as to select more representative unlabelled examples in order to avoid outlier selection in active learning. The proposed model was applied to book review datasets in three different languages. Experiments showed that our model can effectively improve the cross-lingual sentiment classification performance and reduce labelling efforts in comparison with some baseline methods. 相似文献
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The vector space model (VSM) is a textual representation method that is widely used in documents classification. However, it remains to be a space-challenging problem. One attempt to alleviate the space problem is by using dimensionality reduction techniques, however, such techniques have deficiencies such as losing some important information. In this paper, we propose a novel text classification method that neither uses VSM nor dimensionality reduction techniques. The proposed method is a space efficient method that utilizes the first order Markov model for hierarchical Arabic text classification. For each category and sub-category, a Markov chain model is prepared based on the neighboring characters sequences. The prepared models are then used for scoring documents for classification purposes. For evaluation, we used a hierarchical Arabic text data collection that contains 11,191 documents that belong to eight topics distributed into 3-levels. The experimental results show that the Markov chains based method significantly outperforms the baseline system that employs the latent semantic indexing (LSI) method. That is, the proposed method enhances the F1-measure by 3.47%. The novelty of this work lies on the idea of decomposing words into sequences of characters, which found to be a promising approach in terms of space and accuracy. Based on our best knowledge, this is the first attempt to conduct research for hierarchical Arabic text classification with such relatively large data collection. 相似文献
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针对图书、期刊论文等数字文献文本特征较少而导致特征向量语义表达不够准确、分类效果差的问题,本文提出一种基于特征语义扩展的数字文献分类方法。该方法首先利用TF-IDF方法获取对数字文献文本表示能力较强、具有较高TF-IDF值的核心特征词;其次分别借助知网(Hownet)语义词典以及开放知识库维基百科(Wikipedia)对核心特征词集进行语义概念的扩展,以构建维度较低、语义丰富的概念向量空间;最后采用MaxEnt、SVM等多种算法构造分类器实现对数字文献的自动分类。实验结果表明:相比传统基于特征选择的短文本分类方法,该方法能有效地实现对短文本特征的语义扩展,提高数字文献分类的分类性能。 相似文献
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通过分析网格资源访问过程中信任管理问题,借鉴人类社会的信任关系,考虑主观信任的模糊性、复杂性和不确定性等因素,通过对网格资源需求的多方面考虑,引入服务属性概念,提出一种基于多服务属性的网格信誉资源选择模型.服务请求者根据自身喜好对各服务属性进行模糊综合评判,选择交易对象.交易结束后,服务请求者根据服务质量判断服务提供者是否可信,并进行信任度更新.仿真实验表明,该模型能较好地抵御恶意实体的攻击,提高资源选择成功率. 相似文献