Knowledge representation and sharing using visual semantic modeling for diagnostic medical image databases.

Information technology offers great opportunities for supporting radiologists' expertise in decision support and training. However, this task is challenging due to difficulties in articulating and modeling visual patterns of abnormalities in a computational way. To address these issues, well established approaches to content management and image retrieval have been studied and applied to assist physicians in diagnoses. Unfortunately, most of the studies lack the flexibility of sharing both explicit and tacit knowledge involved in the decision making process, while adapting to each individual's opinion. In this paper, we propose a knowledge repository and exchange framework for diagnostic image databases called "evolutionary system for semantic exchange of information in collaborative environments" (Essence). This framework uses semantic methods to describe visual abnormalities, and offers a solution for tacit knowledge elicitation and exchange in the medical domain. Also, our approach provides a computational and visual mechanism for associating synonymous semantics of visual abnormalities. We conducted several experiments to demonstrate the system's capability of matching synonym terms, and the benefit of using tacit knowledge in improving the meaningfulness of semantic queries.     

Fusing semantic aspects for image annotation and retrieval

In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to achieve the task of automatic image annotation and retrieval. In order to model training data precisely, each image is represented as a bag of visual words. Then a probabilistic framework is designed to capture semantic aspects from visual and textual modalities, respectively. Furthermore, an adaptive asymmetric learning algorithm is proposed to fuse these aspects. For each image document, the aspect distributions of different modalities are fused by multiplying different weights, which are determined by the visual representations of images. Consequently, the probabilistic framework can predict semantic annotation precisely for unseen images because it associates visual and textual modalities properly. We compare our approach with several state-of-the-art approaches on a standard Corel dataset. The experimental results show that our approach performs more effectively and accurately.     

Fast semantic diffusion for large-scale context-based image and video annotation

Exploring context information for visual recognition has recently received significant research attention. This paper proposes a novel and highly efficient approach, which is named semantic diffusion, to utilize semantic context for large-scale image and video annotation. Starting from the initial annotation of a large number of semantic concepts (categories), obtained by either machine learning or manual tagging, the proposed approach refines the results using a graph diffusion technique, which recovers the consistency and smoothness of the annotations over a semantic graph. Different from the existing graph-based learning methods that model relations among data samples, the semantic graph captures context by treating the concepts as nodes and the concept affinities as the weights of edges. In particular, our approach is capable of simultaneously improving annotation accuracy and adapting the concept affinities to new test data. The adaptation provides a means to handle domain change between training and test data, which often occurs in practice. Extensive experiments are conducted to improve concept annotation results using Flickr images and TV program videos. Results show consistent and significant performance gain (10 +% on both image and video data sets). Source codes of the proposed algorithms are available online.     

基于视觉语义主题与反馈日志的图像自动标注

为了提高图像标注性能,提出了一种基于视觉语义主题与反馈日志的图像自动标注方法。首 先,提取图像 前景与背景区域,分别进行处理;其次,基于WordNet构建标注词之间的语义关系模型,并 结合概率潜在语义分析(PLSA) 与高斯混合模型(GMM)建立图像底层特征、视觉语义主题与标注     

Knowledge representation and compilation for symptom-disease-testrelationships

A method that represents and compiles expert knowledge for choosing diagnostic tests is presented. Usually, physicians inquire into the patient's complaints, examine him/her physically, and on the assumption of a specific disease or impaired organ system, choose a certain group of laboratory tests designed to examine this assumption. After checking the test results, they may perform another set of tests for further differential diagnosis. This step is repeated until they obtain the final diagnosis. In the proposed method, this knowledge is categorized into three relations. The relation SD is defined on domains symptom/sign and disease, the relation DT is on disease and diagnostic test, and the relation DD is on disease and disease where the first attribute (column) in each relation, respectively, is a primary key, that is, there exists precisely one value, and the second is a set of values. We show that redundant tuples can be reduced or decomposed by checking whether or not there exist tuples whose set of diagnostic tests are equivalent to that of other tuples or are a subset of others. Finally, we can obtain a preferable set of diagnostic tests for given symptoms/signs by using the join of SD and DT, and for further diagnosis, the join of DD and DT is applicable.     

Knowledge representation and natural language processing

In principle, natural language and knowledge representation are closely related. This paper investigates this by demonstrating how several natural language phenomena, such as definite reference, ambiguity, ellipsis, ill-formed input, figures of speech, and vagueness, require diverse knowledge sources and reasoning. The breadth of kinds of knowledge needed to represent morphology, syntax, semantics, and pragmatics is surveyed. Furthermore, several current issues in knowledge representation, such as logic versus semantic nets, general-purpose versus special-purpose reasoners, adequacy of first-order logic, wait-and-see strategies, and default reasoning, are illustrated in terms of their relation to natural language processing and how natural language impacts the issues. We conclude that a significant breakthrough in either natural language processing or in knowledge representation could lead to a breakthrough in the other.     

面向句义及句法的事件检测模型

事件句的句法结构有助于语义理解。针对中文领域的事件检测任务,本文设计了面向句义及句法的事件检测模型(BDD)以增强对事件句的理解能力。以基于来自变压器的双向编码器表示(BERT)的动态词向量为信息源,设计基于依存树的长短时记忆网络模型(D-T-LSTM)以融合学习句法结构及上下文语义,并加入基于依存向量的注意力机制强化对不同句法结构的区分度,在中文突发事件语料库(CEC)上的实验证明了本文模型的有效性,精确率、召回率、F₁值均靠前,且F₁值比基准模型提升了5.4%,召回率提升了0.4%。     

Semantic annotation and transcoding: making Web content moreaccessible

We propose a method for constructing superstructure on the Web using XML and external annotations to Web documents. We have three approaches for annotating documents: linguistic, commentary, and multimedia. The result is annotated documents that computers can understand and process more easily, allowing content to reach a wider audience with minimal overhead     

Soft-decision tracing in fingerprinted multimedia content

To protect intellectual property and distribution rights against dishonest customers in the multimedia content market, fingerprinting schemes that use error-correcting codes help identify users illegally redistributing media. This article presents a traitor-tracing algorithm that takes advantage of soft-decision decoding techniques to find all identifiable traitors.     

Generating multimedia content with cellular automata

Researchers have long used cellular automata (CA), and in general parallel generative devices such as Lindenmayer systems, to produce images, fractals, growth, patterns in two dimensions, and sound. They usually implement these models on special-purpose architectures that often depend on the type of presentation being generated. This paper discusses the developed prototype for generating content, called extended cellular automata with pluggable multimedia elements (ExcapeMe). This prototype acts as an open environment that lets users execute CA in three dimensions and manage simultaneous rendering of CA using different forms of presentation. In principle, we can attach any type of multimedia presentation as a plug-in. The environment already supports ID, 2D, 2.5D, and 3D presentations, and we can apply sound rendering to CA of any dimension. The environment also supports interactively denning, editing, and executing CA as well as defining the mapping between the CA configurations and their actual rendering.     

关于计算机图像理解的知识与知识表达

文章将图像理解活动中所需的知识进行了分类,介绍了各类知识的特性,并介绍了如何在基于知识的图像理解系统中表达这些知识.     

Knowledge representation and construction in hypermedia environments

Hypermedia information systems, because of their inherent qualities of nonlinearity and associative linking, offer the potential of facilitating enhanced knowledge representation and construction for users. These capabilities will be more fully realized if hypermedia information systems are developed and implemented based on constructivist learning theory. This paper examines the qualities of hypermedia systems that enable them to facilitate learning, discusses the background of current learning theories, and provides a brief view of a possible hypermedia system based on constructivist learning principles that could be developed through the use of intelligent autonomous agents.     

Cooperative sparse representation in two opposite directions for semi-supervised image annotation

Recent studies have shown that sparse representation (SR) can deal well with many computer vision problems, and its kernel version has powerful classification capability. In this paper, we address the application of a cooperative SR in semi-supervised image annotation which can increase the amount of labeled images for further use in training image classifiers. Given a set of labeled (training) images and a set of unlabeled (test) images, the usual SR method, which we call forward SR, is used to represent each unlabeled image with several labeled ones, and then to annotate the unlabeled image according to the annotations of these labeled ones. However, to the best of our knowledge, the SR method in an opposite direction, that we call backward SR to represent each labeled image with several unlabeled images and then to annotate any unlabeled image according to the annotations of the labeled images which the unlabeled image is selected by the backward SR to represent, has not been addressed so far. In this paper, we explore how much the backward SR can contribute to image annotation, and be complementary to the forward SR. The co-training, which has been proved to be a semi-supervised method improving each other only if two classifiers are relatively independent, is then adopted to testify this complementary nature between two SRs in opposite directions. Finally, the co-training of two SRs in kernel space builds a cooperative kernel sparse representation (Co-KSR) method for image annotation. Experimental results and analyses show that two KSRs in opposite directions are complementary, and Co-KSR improves considerably over either of them with an image annotation performance better than other state-of-the-art semi-supervised classifiers such as transductive support vector machine, local and global consistency, and Gaussian fields and harmonic functions. Comparative experiments with a nonsparse solution are also performed to show that the sparsity plays an important role in the cooperation of image representations in two opposite directions. This paper extends the application of SR in image annotation and retrieval.     

RDFa语义标注技术综述

RDFa是一项应用于(X)HTML的语义标注技术,弥补了同类标注技术的不足.本文介绍了RDFa(RDF attribute)的设计原则、基本概念,阐述了RDFa基本语法和解析过程,并与同类标注技术进行了比较,分析了其优点及局限性,最后对RDFa的应用前景加以展望.     

基于扩展的语义网络的过程知识表示的研究

文中研究基于语义网络的知识表示,针对目前语义网络框架无法表示过程知识等复杂知识的缺陷,提出了一种称为抽象语义网络的扩展的语义网络,以及基于抽象语义网络的图变换,主要研究了该扩展的语义网络如何表示过程知识,以及利用图变换实现语义网络上的过程变换。     

基于关系模型的语义网络知识表示研究

关系数据库理论基础坚实、技术成熟、产品丰富,多年来一直是存储和处理大数据量的研究和应用首选。如何在关系数据库强大的存储和处理能力基础上建立知识库,一直是知识工程和智能系统研究与应用人员重点研究的内容,也是知识管理走向应用的技术瓶颈。文中提出了语义网络的关系模型,并用实例论述了语义网络知识的关系数据表的存储。