Semantic indexing in structured peer-to-peer networks

The past few years have seen tremendous advances in distributed storage infrastructure. Unstructured and structured overlay networks have been successfully used in a variety of applications, ranging from file-sharing to scientific data repositories. While unstructured networks benefit from low maintenance overhead, the associated search costs are high. On the other hand, structured networks have higher maintenance overheads, but facilitate bounded time search of installed keywords. When dealing with typical data sets, though, it is infeasible to install every possible search term as a keyword into the structured overlay.     

图像语义标注中的叙词查询方法*

为了在图像语义标注领域能更好地反映标注之间的关系,通过对已标注图像的标注进行分析来建立标 注之间的关系,并在此基础上将叙词查询的概念引入到图像语义标注中并提出了基于叙词查询的图像语义标注 方法,把语义标注问题统一在叙词查询与图像的语义关系相结合在统一的框架下,最后通过在Ｃｏｒｅｌ图像数据库中的验证表明,所提出的方法是有效的并且标注率得到了明显的提高。     

Modeling continuous visual features for semantic image annotation and retrieval

Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we firstly extend probabilistic latent semantic analysis (PLSA) to model continuous quantity. In addition, corresponding Expectation-Maximization (EM) algorithm is derived to determine the model parameters. Furthermore, in order to deal with the data of different modalities in terms of their characteristics, we present a semantic annotation model which employs continuous PLSA and standard PLSA to model visual features and textual words respectively. The model learns the correlation between these two modalities by an asymmetric learning approach and then it can predict semantic annotation precisely for unseen images. Finally, we compare our approach with several state-of-the-art approaches on the Corel5k and Corel30k datasets. The experiment results show that our approach performs more effectively and accurately.     

Semantic annotation of soccer videos: automatic highlights identification

Automatic semantic annotation of video streams allows both to extract significant clips for production logging and to index video streams for posterity logging. Automatic annotation for production logging is particularly demanding, as it is applied to non-edited video streams and must rely only on visual information. Moreover, annotation must be computed in quasi real-time. In this paper, we present a system that performs automatic annotation of the principal highlights in soccer video, suited for both production and posterity logging. The knowledge of the soccer domain is encoded into a set of finite state machines, each of which models a specific highlight. Highlight detection exploits visual cues that are estimated from the video stream, and particularly, ball motion, the currently framed playfield zone, players’ positions and colors of players’ uniforms. The highlight models are checked against the current observations, using a model checking algorithm. The system has been developed within the EU ASSAVID project.     

基于相关反馈的图像标注与检索算法

为了提高图像标注与检索的性能,提出了一种基于区域分割与相关反馈的图像标注与检索算法。该算法利用视觉特征与标注信息的相关性,采用基于区域的视觉特征对每幅图像采用聚类方法获得其一组视觉相似图像。通过计算与其距离最近的前3个分类的相似度,然后对这些关键字概率向量进行整合,获得最适合该图像的关键字概率向量,对图像进行标注。利用用户的反馈信息,修正查询关键词与每个分类之间的关系,进一步提高图像检索的准确性。实验结果表明,提出的算法具有更高的查准率与查全率。     

Semantic annotation for knowledge management: Requirements and a survey of the state of the art

While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.     

基于图像分割的语义标注方法

为有效解决图像检索中存在的“语义鸿沟”问题,提出了一种新的语义标注方法。该方法以图像分割为基础,在训练阶段构建图像字典,通过对图像单元颜色、纹理、小波轮廓的分析和描述形成一种结合小波轮廓比对和概率统计的二阶段标注模型,模型针对不同类别的图像分阶段采用相应的标注方法。经实验,应用该模型进行图像检索查全率和查准率都有明显提高,其中查准率最高可提升23.6%,证明该方法更接近人对图像内容的理解,具有良好的标注效果和检索性能。     

基于个性化本体的图像语义标注和检索

针对目前图像检索系统较难实现语义检索的问题,提出了一种新的以本体为核心的图像语义标注和检索模型。构建个性化本体描述图像语义,继而提取基于概念集的图像语义特征并利用本体中“Is-A”关系设计相似性度量方法最终实现语义扩展检索。其难点在于顶级本体向个性化本体进化,以及基于概念集和“Is-A”关系实现语义相似度量的方法。通过系统的初步实现与相关实验的验证,该模型的检索准确度可达88.6%,明显高于传统的基于关键字和基于通用本体的图像检索,实现了图像智能检索功能。     

Personalized retrieval of sports video based on multi-modal analysis and user preference acquisition

In this paper, we present a novel framework on personalized retrieval of sports video, which includes two research tasks: semantic annotation and user preference acquisition. For semantic annotation, web-casting texts which are corresponding to sports videos are firstly captured from the webpages using data region segmentation and labeling. Incorporating the text, we detect events in the sports video and generate video event clips. These video clips are annotated by the semantics extracted from web-casting texts and indexed in a sports video database. Based on the annotation, these video clips can be retrieved from different semantic attributes according to the user preference. For user preference acquisition, we utilize click-through data as a feedback from the user. Relevance feedback is applied on text annotation and visual features to infer the intention and interested points of the user. A user preference model is learned to re-rank the initial results. Experiments are conducted on broadcast soccer and basketball videos and show an encouraging performance of the proposed method.

A probabilistic semantic model for image annotation and multi-modal image retrieval

This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The contribution of our work is three-fold. (1) We propose a probabilistic semantic model in which the visual features and the textual words are connected via a hidden layer which constitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The association of visual features and textual words is determined in a Bayesian framework such that the confidence of the association can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collection crawled from Web is reported to evaluate the prototype system based on the model. In the proposed probabilistic model, a hidden concept layer which connects the visual feature and the word layer is discovered by fitting a generative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype system on 17,000 images and 7736 automatically extracted annotation words from crawled Web pages for multi-modal image retrieval has indicated that the proposed semantic model and the developed Bayesian framework are superior to a state-of-the-art peer system in the literature.     

语义搜索研究综述

语义搜索将语义Web技术引入搜索引擎,改善当前搜索引擎的搜索效果,近年来得到广泛关注.文章介绍了语义搜索领域的研究基础,包括研究现状和常用的研究方法,对语义搜索进行了分类研究和深入分析,语义搜索主要可分为基于传统搜索的增强型语义搜索和基于本体推理的知识型语义搜索;文章指出了语义搜索研究中存在的问题,并对未来开展语义搜索研究进行了总结和展望.     

改进k-means算法在图像标注和检索中的应用

提出一种基于改进的k-means算法的图像标注和检索方法。首先对训练图像进行分割,采用改进的k-means算法对分割后的区域进行聚类。改进的k-means算法首先采用遗传聚类算法确定聚类数k,然后对聚类中心进行选择。在图像标注时,首先通过已标注的图像求出语义概念和聚类区域的关联度,用它作为待标注图像的先验知识,然后结合区域的低层特征,对未标注的图像进行标注。在一个包含1 000幅图像的图像库进行实验,采用标注的语义关键字进行检索,结果表明,提出的方法是有效的。     

Tailored semantic annotation for semantic search

This paper presents a novel method for semantic annotation and search of a target corpus using several knowledge resources (KRs). This method relies on a formal statistical framework in which KR concepts and corpus documents are homogeneously represented using statistical language models. Under this framework, we can perform all the necessary operations for an efficient and effective semantic annotation of the corpus. Firstly, we propose a coarse tailoring of the KRs w.r.t the target corpus with the main goal of reducing the ambiguity of the annotations and their computational overhead. Then, we propose the generation of concept profiles, which allow measuring the semantic overlap of the KRs as well as performing a finer tailoring of them. Finally, we propose how to semantically represent documents and queries in terms of the KRs concepts and the statistical framework to perform semantic search. Experiments have been carried out with a corpus about web resources which includes several Life Sciences catalogs and Wikipedia pages related to web resources in general (e.g., databases, tools, services, etc.). Results demonstrate that the proposed method is more effective and efficient than state-of-the-art methods relying on either context-free annotation or keyword-based search.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

Machine annotation and retrieval for digital imagery of historical materials

Annotating digital imagery of historical materials for the purpose of computer-based retrieval is a labor-intensive task for many historians and digital collection managers. We have explored the possibilities of automated annotation and retrieval of images from collections of art and cultural images. In this paper, we introduce the application of the ALIP (Automatic Linguistic Indexing of Pictures) system, developed at Penn State, to the problem of machine-assisted annotation of images of historical materials. The ALIP system learns the expertise of a human annotator on the basis of a small collection of annotated representative images. The learned knowledge about the domain-specific concepts is stored as a dictionary of statistical models in a computer-based knowledge base. When an un-annotated image is presented to ALIP, the system computes the statistical likelihood of the image resembling each of the learned statistical models and the best concept is selected to annotate the image. Experimental results, obtained using the Emperor image collection of the Chinese Memory Net project, are reported and discussed. The system has been trained using subsets of images and metadata from the Emperor collection. Finally, we introduce an integration of wavelet-based annotation and wavelet-based progressive displaying of very high resolution copyright-protected images. A preliminary version of this work has been presented at the DELOS-NSF Workshop on Multimedia in Digital Libraries, Crete, Greece, June 2003. The work was completed when Kurt Grieb and Ya Zhang were students of The Pennsylvania State University. James Z. Wang and Jia Li are also affiliated with Department of Computer Science and Engineering, The Pennsylvania State University. Yixin Chen is also with the Research Institute for Children, Children's Hospital, New Orleans.     

Web资源的多粒度语义标注及其应用技术研究

当前的Web搜索引擎获得的搜索结果都是基于关键字标注的Web文档、页面或链接,不支持对文档内部信息的检索。为支持Wcb资源内部信息的检索,研究多粒度语义标注,即按树根结点、分支结点、叶子结点及资源信息元为粒度单位对Web资源进行组织管理,并在此基础上探讨基于本体的搜索技术。初步的分析和实验表明,这样可以提高从形式多样的海量Web资源中获取所需信息的效率。     

OWLS-MX: A hybrid Semantic Web service matchmaker for OWL-S services

In this paper, we describe the first hybrid Semantic Web service matchmaker for OWL-S services, called OWLS-MX. It complements crisp logic-based semantic matching of OWL-S services with token-based syntactic similarity measurements in case the former fails. The results of the experimental evaluation of OWLS-MX provide strong evidence for the claim that logic-based semantic matching of OWL-S services can be significantly improved by incorporating non-logic-based information retrieval techniques. An additional analysis of false positives and false negatives of the hybrid matching filters of OWLS-MX led to an even further improved matchmaker version called OWLS-MX2.     

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。