一种基于本体的图像检索方法

提出一种基于本体的图像检索方法。该方法结合特定领域专家知识和对象例图,采用视觉对象本体来描述图像内特定对象的视觉特征,从而构建该领域包含视觉描述的知识库。在检索过程中,利用知识库内的对象的视觉本体描述和目标图像库内的图像低层特征相匹配执行图像检索任务,从而实现在高层次语义上的图像检索。实验结果表明了该方法的有效性和可行性,并在一定程度上缩小了视觉低层特征同图像高层语义的鸿沟。     

基于知识本体的语义信息检索框架设计

基于关键词匹配的信息检索方式不能反映出被检关键字在现实世界中的语义，因此这种检索方式不可避免地导致查准率和查全率低的缺陷，而概念检索中的主体词典表达领域知识的能力有限。提出了一个基于知识本体的能够实现语义信息检索的多主体系统，它包括描述信息收集、存储、语义匹配和语义相关性扩展等几个主要部分，可以在很大程度上提高检索结果的查准率和查全率。     

基于本体的数字家庭SOA服务发现机制

SOA的关键是服务,它为WebService提供了一个架构,使用该架构组织家庭服务可以实现跨平台的服务访问。然而SOA架构大多数规范如SOAP、UDDI和WSDL等均是基于XML的,所描述的服务缺乏语义信息,因此在检索过程无法有效处理一词多义和多词一义等语法问题。通过引入本体技术,建立家庭服务领域本体,从服务的功能、内容和设备三个方面利用功能概念本体、内容本体和设备本体进行语义标注,并扩展WSDL为S-WSDL来描述家庭服务。基于本体的家庭服务发现过程分为三个阶段:查找功能、过滤服务和参数类型匹配,充分利用本体的语义优势实现了智能数字家庭服务发现。     

Semantic rule based image visual feature ontology creation

Multimedia is one of the important communication channels for mankind. Due to the advancement in technology and enormous growth of mankind, a vast array of multimedia data is available today. This has resulted in the obvious need for some techniques for retrieving these data. This paper will give an overview of ontology-based image retrieval system for asteroideae flower family domain. In order to reduce the semantic gap between the low-level visual features of an image and the high-level domain knowledge, we have incorporated a concept of multi-modal image ontology. So, the created asteroideae flower domain specific ontology would have the knowledge about the domain and the visual features. The visual features used to define the ontology are prevalent color, basic intrinsic pattern and contour gradient. In prevalent color extraction, the most dominant color from the images was identified and indexed. In order to determine the texture pattern for a particular flower, basic intrinsic patterns were used. The contour gradients provide the information on the image edges with respect to the image base. These feature values are embedded in the ontology at appropriate slots with respect to the domain knowledge. This paper also defines some of the query axioms which are used to retrieve appropriate information from the created ontology. This ontology can be used for image retrieval system in semantic web.     

基于本体的构件聚类研究

为了消除自然语言对构件文本信息描述的二义性以及增强术语间的语义关系,文中采用领域本体的思想,给出了一个基于人工智能领域本体的软件构件聚类模型和基于该模型的聚类算法。该模型通过分析领域的共同概念,形成领域本体知识库,提供领域内一致认可的术语,用于匹配对构件文本描述所使用的自然语言。给出的算法通过与基于传统空间向量的K—Means算法分析比较,验证了该算法是有效的,实现了对软件构件更合理的聚类,提高了构件检索的效率和准确性。     

一种基于本体与描述文本的网络图像语义标注方法

网络图像语义自动标注是实现对互联网中海量图像管理和检索的有效途径,而自动有效地挖掘图像语义是实现自动语义标注的关键。网络图像的语义蕴含于图像自身,但更多的在于对图像语义起不同作用的各种描述文本,而且随着图像和描述知识的变化,描述文本所描述的图像语义也随之变化。提出了一种基于领域本体和不同描述文本语义权重的自适应学习的语义自动标注方法,该方法从图像的文本特征出发考查它们对图像语义的影响,先通过本体进行有效的语义快速发现与语义扩展,再利用一种加权回归模型对图像语义在其不同类型描述文本上的分布进行自适应的建模,进而实现对网络图像的语义标注。在真实的Wcb数据环境中进行的实验中,该方法的有效性得到了验证。     

Image retrieval with a multi-modality ontology

Ontologies represent domain concepts and relations in a form of semantic network. Many research works use ontologies in the information matchmaking and retrieval. This trend is further accelerated by the convergence of various information sources supported by ontologies. In this paper, we propose a novel multi-modality ontology model that integrates both the low-level image features and the high-level text information to represent image contents for image retrieval. By embedding this ontology into an image retrieval system, we are able to realize intelligent image retrieval with high precision. Moreover, benefiting from the soft-coded ontology model, this system has good flexibility and can be easily extended to the larger domains. Currently, our experiment is conducted on the animal domain canine. An ontology has been built based on the low-level features and the domain knowledge of canine. A prototype retrieval system is set up to assess the performance. We compare our experiment results with traditional text-based image search engine and prove the advantages of our approach.     

基于本体的图像语义标注与检索模型

存储于计算机上日益庞大的数字图像存在语义表达隐含、存储孤立、检索不便等缺点,导致图像应用效率低。该文提出基于本体的图像语义标注与检索模型ImageQ,其具有层次及模块化特点且与领域本体相独立,支持领域本体进化和系列推理深度不同的图像检索方法。基于ImageQ的实验系统运行结果表明,该模型的图像管理性能良好,实现了图像智能检索功能。     

安全事件综合分析系统框架及关键技术

存储于计算机上日益庞大的数字图像存在语义表达隐含、存储孤立、检索不便等缺点,导致图像应用效率低.该文提出基于本体的图像语义标注与检索模型ImageQ,其具有层次及模块化特点且与领域本体相独立,支持领域本体进化和系列推理深度不同的图像检索方法.基于ImageQ的实验系统运行结果表明,该模型的图像管理性能良好,实现了图像智能检索功能.     

矿山语义物联网自动语义标注方法

针对目前矿山领域异构数据融合时先验知识获取困难、物联网本体库实时性差、实例对象数据手动标注方式效率较低等问题,提出了一种矿山语义物联网自动语义标注方法。给出了传感数据语义化处理框架:一方面,确定本体的专业领域和范畴,通过重用流注释本体(SAO)构建领域本体,作为驱动语义标注的基础;另一方面,使用机器学习方法对感知数据流进行特征提取与数据分析,从海量数据中挖掘出概念间的关系;通过数据挖掘知识来驱动本体的更新与完善,实现本体的动态更新、拓展与更精确的语义标注,增强机器的理解力。以矿井提升系统主轴故障为例阐述从本体到实例化的语义标注过程:结合领域专家知识及本体重用,采用"七步法"建立矿井提升系统主传动故障本体;为了加强实例数据属性描述的准确性,使用主成分分析法(PCA)与K-means聚类方法对数据集进行降维和分组,提取出数据属性与概念的关系;通过基于语义Web的规则语言(SWRL)标注具体先行条件与后续概念的关系,优化领域本体。实验结果表明:在本体实例化过程中,可利用机器学习技术从传感数据中自动提取概念,实现传感数据的自动语义标注。     

Multimedia ontology matching by using visual and textual modalities

Ontologies have been intensively applied for improving multimedia search and retrieval by providing explicit meaning to visual content. Several multimedia ontologies have been recently proposed as knowledge models suitable for narrowing the well known semantic gap and for enabling the semantic interpretation of images. Since these ontologies have been created in different application contexts, establishing links between them, a task known as ontology matching, promises to fully unlock their potential in support of multimedia search and retrieval. This paper proposes and compares empirically two extensional ontology matching techniques applied to an important semantic image retrieval issue: automatically associating common-sense knowledge to multimedia concepts. First, we extend a previously introduced textual concept matching approach to use both textual and visual representation of images. In addition, a novel matching technique based on a multi-modal graph is proposed. We argue that the textual and visual modalities have to be seen as complementary rather than as exclusive sources of extensional information in order to improve the efficiency of the application of an ontology matching approach in the multimedia domain. An experimental evaluation is included in the paper.     

面向特定目标自识别的交通图像语义检索方法

为了从海量的道路交通图像中检索出违反交通法规的图像,提出了一种特定目标自识别的语义图像检索方法。首先,通过交通领域专家建立交通领域本体及道路交通规则描述;然后,通过卷积神经网络（CNN）对交通图像的特征进行提取,并结合改进的支持向量机决策树（SVM-DT）算法对图像特征进行分类的策略,对交通图像中的特定目标及目标间空间位置关系进行自动识别,并映射成为相应的本体实例及其对象之间的关联关系（规则实例）;最后,利用本体实例和规则实例,通过推理得到语义检索结果。实验结果表明,相比关键字和本体交通图像语义检索方法,所提方法具有更高的准确率、召回率和检索效率。     

Ontological foundations for experimental science knowledge bases

The framework for representing domain ontologies presented in this paper extends existing ontological models and traditional frame-based formalisms. This work was motivated by the representational challenges posed by the domains of experimental sciences (biology, chemistry, physics) and the task of intelligent text retrieval. A detailed ontology for the field of experimental molecular biology is presented, which is used to illustrate the need for and application of the features of the framework. An extended frame-based formalism is defined to support these features. The ability of the framework to support intelligent retrieval from a knowledge base of molecular-biology research papers is demonstrated by providing answers to queries that could not be fully answered using previous approaches. The extensions to ontological framework include : category conversions, processes that change the category or identity of their participants; object histories to track substances through a series of experimental processes, including category conversions; object complexes, temporary configurations of objects with properties of their own; and process complexes, groups or sequences of interrelated actions that comprise an experimental technique or procedure. Features of the frame-based formalism include: slot groups for identifying sets of relations that license common inferences; and open-filler classes that combine knowledge of likely slot values with the ability to handle unexpected values. Evaluation techniques that are used to assess the adequacy of the ontology are presented: the ontology's conceptual coverage of the domain, its potential usefulness in improving the quality of query answering, and its formal consistency and reusability by the knowledge-sharing community are evaluated.     

Visual Ontology Construction for Digitized Art Image Retrieval

Current investigations on visual information retrieval are generally content-based methods. The significant difference between similarity in low-level features and similarity in high-level semantic meanings is still a major challenge in the area of image retrieval. In this work, a scheme for constructing visual ontology to retrieve art images is proposed. The proposed ontology describes images in various aspects, including type ＆ style, objects and global perceptual effects. Concepts in the ontology could be automatically derived. Various art image classification methods are employed based on low-level image features. Non-objective semantics are introduced, and how to express these semantics is given. The proposed ontology scheme could make users more naturally find visual information and thus narrows the “semantic gap”. Experimental implementation demonstrates its good potential for retrieving art images in a human-centered manner.     

基于本体的智能信息检索系统

近年来,基于本体的智能信息检索系统已成为智能信息检索系统领域最为活跃的研究方向之一.如何利用本体进一步提高其检索性能和智能性,成为基于本体的智能信息检索系统的主要研究目标.从面向过程的角度,对近几年基于本体的智能信息检索系统的研究进展进行了综述,对其框架、所需本体知识的获取和使用、关键技术、性能评测等进行了前沿概括、比较和分析.最后,对基于本体的智能信息检索系统有待深入研究的难点和热点进行了展望.     

A CBR system for injection mould design based on ontology: A case study

Case-based reasoning (CBR) often shows significant promise for improving the effectiveness of design support in mould design, which is a domain strong in practice but poor in theory. However, existing CBR systems lack semantic understanding, which is important for intelligent knowledge retrieval in design support system. This hinders the application of CBR in injection mould design. In order to develop an intelligent CBR system and meet the need of design support for injection mould design, this paper integrates ontology technology into a CBR system by constructing domain ontology as case-base with a new method, in which two means of acquisition are combined, one is acquiring ontology from existing ontologies, the other from established engineering knowledge resources, and proposing a new semantic retrieval method as the first grade case retrieval. Numerical measurement is also employed as the second grade case retrieval, which adopts various methods to calculate different types of attribute values. A case is executed to illustrate the use of proposed CBR system, then a lot of experiments are organized to evaluate its performance and the result shows that the proposed approach outperforms existing CBR systems.     

基于智能体和本体的WEB图像检索模型

针对基于关键词WEB图像检索中的语义缺失问题,利用本体的方法描述WEB图像的语义特征,构建了基于智能体和语义特征的WEB图像检索模型,该模型以领域Ontology描述WEB图像的语义特征,通过多个Agent模块分工协作,完成满足用户请求的WEB图像检索.并在Corel提供的图像上进行了仿真实验,验证了该模型解决了基于关键词WEB图像检索模型中的语义缺失问题,提高了WEB图像检索速度和准确率.     

语义网检索中的知识收集方法研究

针对传统信息检索中存在的缺乏知识表示和语义处理能力低的问题,本文提出了一个基于本体的语义网信息检索框架,该框架包括知识收集与储存层、人机交互层和知识处理层。对该框架中知识收集与储存层的关键技术及实现进行了介绍,主要包括领域本体库的构建、元数据的提取和知识库的构建等功能。其中,对元数据提取中的核心技术的实现,如领域本体库的解析、文件预处理、语义标注、知识抽取等作了详尽的介绍。