基于本体的文档语义标注改进方法

在领域本体知识的语义环境和资源文档结构基础上,提出一种文档语义标注改进方法,分析、计算标签一文档的词频相关性和语义环境在局部窗口的共现性,实现对各类文档资源的语义标注.该方法首先提取出文档资源的纯文本内容,并分解出子句、句和段落集合.然后,对于每个具体的领域知识项,在本体知识库中寻找其语义环境信息.最后,按照7条相关度规则,分别计算出这些信息与分解后文档内容的相关度,从而完成整个文档库内和知识库内的综合计算,得到该项知识与文档资源的最终相关度.卖验结果显示,该方法能够依据领域本体,有效地对互联网中大量以网页等形式存在的多种类文档知识资源进行自动语义标注.     

Bridge Ontology： A Multi-Ontologies.Based Approach for Semantic Annotation

Representing the relationships between ontologies is the key problem of semantic annotations based on multi ontologies. Traditional approaches only had the ability of denoting the simple concept subsumption relations between ontologies. Through analyzing and classifying the relationships between ontologies, the idea of bridge ontology was proposed, which had the powerful capability of expressing the complex relationships between concepts and relationships between relations in muhi ontologies. Meanwhile, a new approach employing bridge ontology was proposed to deal with the multi ontologies-based semantic annotation problem. The bridge ontology is a peculiar ontology, which can be created and maintained conveniently, and is effective in the multi-ontologies based semantic annotation. The approach using bridge ontology has the advantages of low cost, scalablc, robust in the web circumstance, and avoiding the unnecessary ontology extending and integration.     

Exploiting PLSA model and conditional random field for refining image annotation

This paper presents a new method for refining image annotation by integrating probabilistic latent semantic analysis(PLSA) with conditional random field(CRF).First a PLSA model with asymmetric modalities is constructed to predict a candidate set of annotations with confidence scores,and then model semantic relationship among the candidate annotations by leveraging conditional random field.In CRF,the confidence scores generated by the PLSA model and the Flickr distance between pairwise candidate annotations are considered as local evidences and contextual potentials respectively.The novelty of our method mainly lies in two aspects:exploiting PLSA to predict a candidate set of annotations with confidence scores as well as CRF to further explore the semantic context among candidate annotations for precise image annotation.To demonstrate the effectiveness of the method proposed in this paper,an experiment is conducted on the standard Corel dataset and its results are compared favorably with several state-of-the-art approaches.     

Web服务语义标注方法

为了对W eb服务语义信息进行描述,提出了一种W eb服务语义标注方法。以现有的W eb服务描述语言(W SDL)文件为基础,充分利用W SDL中XM L schem a的语义信息与本体实体的相似性,从W SDL文件中抽取语义信息,并利用实体之间的名称相似度与结构相似度进行语义标注,生成基于OW L-S的W eb服务语义描述。用该方法对多个领域的W eb服务进行了语义标注,标注正确率在92.5%以上,较好地解决了W eb服务的语义标注问题。     

基于领域本体的信息搜索模型

针对目前的搜索模型局限于语法层次上关键词匹配的特点,以领域本体作为知识组织方式,提出了一种语义环境下基于本体的信息搜索模型.在此模型的基础上,分别提出了文档语义标注算法和搜索词语义扩展算法,两种算法分别对文档集语义分析和搜索词语义关系理解,实现双向语义信息搜索的目的.实验结果表明,提出的信息搜索方法能够克服关键词匹配搜索的不足,获得较好的搜索效果     

标注工具中OWL本体编辑功能的实现研究

基于本体的语义标注工具使用已有本体在Web页面中插入语义元数据信息,从而使Web页的内容机器可识别,是将现有Web提升为语义Web的有效方法之一.大多数标注工具仅支持使用已存在的本体词汇来标注Web页,不具备标注过程中新建本体或在已有本体中添加新词汇从而补充标注词汇的本体编辑功能.针对语义标注中的本体编辑功能的特点及存在问题进行研究,并从实现的角度对OWL本体编辑进行讨论.     

基于语义Web的网页推荐模型

如何使用语义 Web技术构建网页推荐模型是一个有着广泛应用前景的研究课题。语义 Web下的网页用本体标注 ,该模型自动采集被标注网页上的语义信息 ,利用网页中的标注信息和相应的本体概念对网页进行分类 ,将分类结果存放在数据库中。同时 ,在这个模型下 ,用户的兴趣存放在DAML (DARPA agent markup language)格式的文件中。通过用户兴趣和网页类别的匹配 ,就能够给用户推荐需要的网页 ,在推荐网页时 ,该模型还采用支持向量机用于分类用户。实验结果显示了该模型比传统的网页推荐模型准确率更高     

面向知识级应用的多维语义本体构建

本体作为知识的承载者被信息科学领域引入,用来解决知识表示和知识组织方面的问题。基于语义本体理论,提出了多维语义本体模型,从不同维度对世界知识进行建模。基于知识的抽象层次,在多语言、多领域知识之间建立了语义关联,在此基础上构建了多维语义本体,用于解决信息共享时语义缺乏和多语言知识关联的关键问题。对多维语义本体进行了分析和横向比较,并对将来构建过程中面临的主要问题和挑战进行了探讨。     

经济学知识网格的构建

通过对相关工作的研究,结合经济学知识网格的实践,设计和实现了一个ESWP平台来辅助开发者以半自动方式构建知识网格.该平台包括三个主要的功能模块:本体构建模块、语义标注模块和进化管理模块.它们分别采用基于角色的用户管理和多版本机制来支持多用户协同的本体开发;采用支持向量机(SVM)和一组标注规则来支持文档级标注和词汇级标注;采用边标注边进化的方法来支持资源的进化管理.目前该平台已成功地应用于一个经济学知识网格原型———经济学学科知识检索系统.     

Semantic image annotation based on GMM and random walk model

Automatic image annotation has been an active topic of research in computer vision and patternrecognition for decades.A two stage automatic image annotation method based on Gaussian mixturemodel (GMM) and random walk model (abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization (RPEM) algorithm is employed to estimatethe posterior probabilities of each annotation keyword.Subsequently, a random walk processover the constructed label similarity graph is implemented to further mine the potential correlations ofthe candidate annotations so as to capture the refining results, which plays a crucial role in semanticbased image retrieval.The contributions exhibited in this work are multifold.First, GMM is exploitedto capture the initial semantic annotations, especially the RPEM algorithm is utilized to train themodel that can determine the number of components in GMM automatically.Second, a label similaritygraph is constructed by a weighted linear combination of label similarity and visual similarity ofimages associated with the corresponding labels, which is able to avoid the phenomena of polysemyand synonym efficiently during the image annotation process.Third, the random walk is implementedover the constructed label graph to further refine the candidate set of annotations generated byGMM.Conducted experiments on the standard Corel5k demonstrate that GMM-RW is significantlymore effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.     

基于本体的音频语义检索模型

通过分析现有音频检索中存在的问题,提出了一种对语音数据进行语义自动标注的方法,建立了一种新的音频本体库的检索模型,并对今后的研究方向进行了展望。     

Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

In recent years, multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas, especially for automatic image annotation, whose purpose is to provide an efficient and effective searching environment for users to query their images more easily.In this paper, a semi-supervised learning based probabilistic latent semantic analysis ( PL-SA) model for automatic image annotation is presenred.Since it' s often hard to obtain or create la-beled images in large quantities while unlabeled ones are easier to collect, a transductive support vector machine ( TSVM) is exploited to enhance the quality of the training image data.Then, differ-ent image features with different magnitudes will result in different performance for automatic image annotation.To this end, a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible.Finally, a PLSA model with asymmetric mo-dalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores.Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PL-SA for the task of automatic image annotation.     

混合语义模型的产品知识文档检索

为解决产品知识文档检索过程中遇到的问题,提出一种基于混合语义模型的检索方法.该方法将传统的用户查询需求扩展为用户偏好、语境和用户查询混合而成的语义集合,并对知识文档和用户需求进行基于本体的模糊概念表达.对于知识文档,选择领域本体的叶节点来构造文本概念向量,根据概念在本体图中的深度、携带的信息量,及出现在文档与语料库中的频度来计算权重.同样采用本体表达知识语境与查询语义,建立用户偏好模型.针对检索模型的不同组成,阐述了相应的相似度计算方法,采用概念的语义距离计算用户当前语境和文档语境之间的相似度,用余弦法计算查询语义、用户偏好与文档的相似度.最后用实验验证了该方法的检索效果优于传统的向量空间方法.     

基于领域本体的自动化语义标注方法的研究

介绍了语义网,本体以及语义标注的基本概念,对现有的语义标注方法以及技术进行了说明和分析.针对目前互联网上含有大量语义信息的HTML文档,提出了一种自动化的语义标注的方法.该方法对HTML文档进行结构分析,并参照词汇数据库Hownet和领域本体对文档进行语义分析,找出文档对应的语义分类树,给文档加上语义标签.以电子消费品领域的HTML文档为实验对象,实验结果证明了该方法具有一定的可行性.     

基于叙词表的油藏构造知识图谱

为采用知识图谱技术实现对油藏构造知识的整合,解决在本体建模过程中,很少将标准融合到本体模型 中,且在转换过程中存在对叙词概念粒度划分较粗或完全不对叙词进行类型划分等问题,提出一种基于叙词表 构建知识图谱的新方法,即标注原叙词表,建立映射规则,构造转换算法和生成知识图谱。其中标注、映射规 则建立与转换算法定义了构建知识图谱标准化过程。最后,选取油藏构造领域的部分知识,采用所提方法构建 知识图谱,其结果表明了该方法的可行性。     

融合BabelNet 的多语言智能信息检索模型

传统的跨语言信息检索存在翻译映射准确度低和查询扩展后语义偏离等问题。为此提出结合统计学和 本体论的方法构建多语言信息检索模型,通过使用统计翻译解决翻译映射歧义问题,使用多本体BabelNet 等减 少语义关联度损失。由于本体包含大量概念联系,因此使用本体作为语义层表示设计了语义权重算法,并将其 构建在BM25F 统计信息检索模型上作为用户反馈的排序算法。最后根据建立的模型设计实现了多语言信息检 索原型系统,并用基于爬虫技术获取的数据测试集对模型进行测试,实验结果表明,该模型平均查准率高于传 统的基于机器翻译的信息检索模型。     

唐卡领域本体研究与构建

分析并构建唐卡领域本体,以实现基于语义的唐卡图像标注与检索.在唐卡领域专家的指导下分析唐卡的分类体系,给出唐卡本体相关的定义和公理,定义了相关概念、对象、属性、实例及约束条件,并基于Protégé构建了唐卡领域本体.最后对其进行一致性分析,实现了基于语义的唐卡知识库查询,进一步验证了所构建唐卡领域本体的合理性.     

当前基于本体的语义标注工具的分析

基于本体的语义标注工具使用已有本体在Web页面中插入语义元数据信息,从而使Web页的内容机器可识别,它完成的是构建语义Web的基础性工作.本文将对当前基于本体的语义标注工具进行分析和比较,并总结和探讨基于本体的语义标注工具的发展趋势.     

KIS/OBT: A Tool to Build Ontology for Knowledge Integrating System

After many years research work, many intelligent systems based on knowledge have been created.But the differences in creating methods and applying background contexts make it difficult to share and reuse knowledge.This situation leads to the difficulty of building knowledge system.Without method using the existing system efficiently, people often build knowledge sys-tem from scratch.In order to solve this problem, we use ontology as a foundation to realize know-ledge sharing and reusing.As an important research area in AI, ontology building method has not acquired a common view.The method for building ontology was discussed, it's principles and implementation.