基于知识的UML图形文档自动生成

基于领域本体的需求获取使得用领域术语描述用户需求,并进行应用模型的自动分析成为可能,以尽可能地保证需求描述的正确性和完整性。从以领域概念为背景的应用模型,到以软件概念为基础的应用软件模型,是需求工程要完成的一个实质性跨越。UML是目前软件需求描述的一个实际上的标准。该文主要论述了从基于领域本体的应用需求描述到基于UML的软件需求文档的自动转换过程,文章提出建立从领域本体概念到UML概念映射规则库,以及根据UML概念填写用XML表示的UML图元模板的规则库,设计并实现了一个基于规则的,从应用需求概念到UML图元模板的自动转换工具。     

一种支持可靠的语义互操作的本体管理方法

在语义网中,本体被广泛用于描述信息资源的含义。当交互的信息系统所使用的本体之间存在差异时,需要建立本体映射。本体映射是实现信息资源映射,以及信息系统互操作的前提。提出了一个支持可靠的语义互操作的方法,详细介绍了该方法的基本思想,包含的信息模型,以及实现平台的基本架构。部分内容基于正在制定的国际标准ISO/IEC19763-3本体注册元模型。     

Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.     

Proof-of-concept of an information visualization classification approach based on their fine-grained features

The misinformation problem affects the development of the society. Misleading content and unreliable information overwhelm social networks and media. In this context, the use of data visualizations to support news and stories is increasing. The use of misleading visualizations both intentionally or accidentally influence in the audience perceptions, which usually are not visualization and domain experts. Several factors influence o accurately tag a visualization as confusing or misleading. In this paper, we present a machine learning approach to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine-grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. We automatically generated visualizations from a tri-variate dataset through the software product line paradigm and manually labelled them to obtain a training dataset. The results support the viability of the proposal as a tool to support journalists, audience and society in general, not only to detect confusing visualizations, but also to select the visualization that supports a previous defined task according to the data domain.     

Comparing Bar Chart Authoring with Microsoft Excel and Tangible Tiles

Providing tools that make visualization authoring accessible to visualization non‐experts is a major research challenge. Currently the most common approach to generating a visualization is to use software that quickly and automatically produces visualizations based on templates. However, it has recently been suggested that constructing a visualization with tangible tiles may be a more accessible method, especially for people without visualization expertise. There is still much to be learned about the differences between these two visualization authoring practices. To better understand how people author visualizations in these two conditions, we ran a qualitative study comparing the use of software to the use of tangible tiles, for the creation of bar charts. Close observation of authoring activities showed how each of the following varied according to the tool used: 1) sequences of action; 2) distribution of time spent on different aspects of the InfoVis pipeline; 3) pipeline task separation; and 4) freedom to manipulate visual variables. From these observations, we discuss the implications of the variations in activity sequences, noting tool design considerations and pointing to future research questions.     

How information visualization novices construct visualizations

It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. We derived abstract models from our observations that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process.     

基于Ontology的自然语言理解

本文分析传统意义上基于知识的自然语言理解(KB—NLU)和基于Ontolqgy的自然语言理解系统的基本模型，Ontology是概念化的描述，以及Ontology，与语言知识的结合方式的三种类型：世界知识型、词汇语义型、句法语义型。     

VN-KIM IE: Automatic Extraction of Vietnamese Named-Entities on the Web

The most fascinating advantage of the semantic web would be its capability of understanding and processing the contents of web pages automatically. Basically, the semantic web realization involves two main tasks: (1) Representation and management of a large amount of data and metadata for web contents; (2) Information extraction and annotation on web pages. On the one hand, recognition of named-entities is regarded as a basic and important problem to be solved, before deeper semantics of a web page could be extracted. On the other hand, semantic web information extraction is a language-dependent problem, which requires particular natural language processing techniques. This paper introduces VN-KIM IE, the information extraction module of the semantic web system VN-KIM that we have developed. The function of VN-KIM IE is to automatically recognize named-entities in Vietnamese web pages, by identifying their classes, and addresses if existing, in the knowledge base of discourse. That information is then annotated to those web pages, providing a basis for NE-based searching on them, as compared to the current keyword-based one. The design, implementation, and performance of VN-KIM IE are presented and discussed.     

Contextualizing ontologies

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party’s subjective view of a domain. In this paper, we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.     

Mutation Mining—A Prospector's Tale

Protein structure visualization tools render images that allow the user to explore structural features of a protein. Context specific information relating to a particular protein or protein family is, however, not easily integrated and must be uploaded from databases or provided through manual curation of input files. Protein Engineers spend considerable time iteratively reviewing both literature and protein structure visualizations manually annotated with mutated residues. Meanwhile, text mining tools are increasingly used to extract specific units of raw text from scientific literature and have demonstrated the potential to support the activities of Protein Engineers. The transfer of mutation specific raw-text annotations to protein structures requires integrated data processing pipelines that can co-ordinate information retrieval, information extraction, protein sequence retrieval, sequence alignment and mutant residue mapping. We describe the Mutation Miner pipeline designed for this purpose and present case study evaluations of the key steps in the process. Starting with literature about mutations made to protein families; haloalkane dehalogenase, bi-phenyl dioxygenase, and xylanase we enumerate relevant documents available for text mining analysis, the available electronic formats, and the number of mutations made to a given protein family. We review the efficiency of NLP driven protein sequence retrieval from databases and report on the effectiveness of Mutation Miner in mapping annotations to protein structure visualizations. We highlight the feasibility and practicability of the approach. Funding project: Ontologies, the semantic web and intelligent systems for genomics Génome Québec, 630, boul. René-Lévesque Ouest, bureau 2660, Montréal (Québec) H3B 1S6, e-mail: gqinfo@genomequebec.com     

A hybrid ontology approach for integration of obsolescence information

Information sharing among distributed obsolescence management systems is a challenge because of the heterogeneity of data (data with different forms and representations). Indeed, this is the main hurdle that exists for current tools managing product obsolescence. This paper presents a hybrid ontology approach for the integration of obsolescence information that combines a global ontology that provides a shared vocabulary for the specification of the semantics of obsolescence domain knowledge, along with local ontologies that describe structures of multiple data sources distributed in various obsolescence management tools. A procedure is provided for mapping local ontologies to the global ontology by quantifying relationships between classes and identifying groups of classes with a clustering method. Ontologies and rules of identifying relationships are realized with OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language). With the application of the hybrid ontology approach, a unified view of data is provided to support decision making for efficient obsolescence management and a structure where new sources of information can be easily added with little modification in the future.     

基于奇异值分解的中文Ontology自动学习技术

语义Web是一个美好的构想，Ontology在语义Web中起着举足轻重的作用，它不仅能为人类用户而且能为软件agent提供从语法层次到语义层次上的互操作性。目前Web上主要是各种布局的HTML文档，未来的语义Web页面将是各种领域Ontology的实例以及到其它实例上的链接，因此语义Web的成功强烈依赖于Ontology的增殖，方便快捷地构造各领城Ontology是实现语义Web的关健。该文提出一种基于奇异值分解的中文Ontology自动学习技术，这种技术的特点是其简易性以及准确的数学理论基础。     

语义虚拟环境本体可视化模型的研究

当前信息可视化所面临的一个瓶颈就是,需要一种模型来把场景图形内容和特定领域的语义信息进行有效集成,使用户可以有效地解释个性化可视化信息。基于X3D标准以及本体用本体描述语言OWL描述X3D标准,构建了一个X3D标准本体和一个映射本体来实现X3D标准本体和其他具体领域本体之间类和属性的映射,以达到丰富虚拟场景语义的目的。基于X3D标准本体和映射本体设计了语义虚拟环境本体可视化模型,从而为具体领域本体实现可视化提供了一种方法。实验结果证明了该模型的可行性。     

Particle flurries

Particle Flurries is an interactive approach to 3D flow visualization. The approach produces a "synoptic visualization" and is used to examine both internal and external flows. Synoptic visualizations give viewers a synopsis of all flow features simultaneously. Good examples of 2D synoptic visualizations are weather maps from both TV news reports and online Web pages. The human visual system is adept at finding patterns within larger contexts, and we hypothesize that synoptic visualization methods will help users find unexpected features more quickly and thus speed the understanding of complex 3D time-varying flows. Particle Flurries (PF) is our effort toward a synoptic visualization of complex pulsatile 3D flow.     

Web classification of conceptual entities using co-training

Social networking websites, which profile objects with predefined attributes and their relationships, often rely heavily on their users to contribute the required information. We, however, have observed that many web pages are actually created collectively according to the composition of some physical or abstract entity, e.g., company, people, and event. Furthermore, users often like to organize pages into conceptual categories for better search and retrieval, making it feasible to extract relevant attributes and relationships from the web. Given a set of entities each consisting of a set of web pages, we name the task of assigning pages to the corresponding conceptual categories conceptual web classification. To address this, we propose an entity-based co-training (EcT) algorithm which learns from the unlabeled examples to boost its performance. Different from existing co-training algorithms, EcT has taken into account the entity semantics hidden in web pages and requires no prior knowledge about the underlying class distribution which is crucial in standard co-training algorithms used in web classification. In our experiments, we evaluated EcT, standard co-training, and other three non co-training learning methods on Conf-425 dataset. Both EcT and co-training performed well when compared to the baseline methods that required large amount of training examples.     

Visualization techniques for mining large databases: a comparison

Visual data mining techniques have proven to be of high value in exploratory data analysis, and they also have a high potential for mining large databases. In this article, we describe and evaluate a new visualization-based approach to mining large databases. The basic idea of our visual data mining techniques is to represent as many data items as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. The major goal of this article is to evaluate our visual data mining techniques and to compare them to other well-known visualization techniques for multidimensional data: the parallel coordinate and stick-figure visualization techniques. For the evaluation of visual data mining techniques, the perception of data properties counts most, while the CPU time and the number of secondary storage accesses are only of secondary importance. In addition to testing the visualization techniques using real data, we developed a testing environment for database visualizations similar to the benchmark approach used for comparing the performance of database systems. The testing environment allows the generation of test data sets with predefined data characteristics which are important for comparing the perceptual abilities of visual data mining techniques     

Flexible Linked Axes for multivariate data visualization

Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising.     

基于核心概念集的多语言Ontology

随着Web资源的日益丰富,人们需要跨语言的知识共享和信息检索。一个多语言Ontology可以用来刻画不同语言相关领域的知识,克服不同文化和不同语言带来的障碍。对现有的构建多语言Ontology方法进行分析和比较,提出一种基于核心概念集的多语言Ontology的构建方法,用一个独立于特定语言的Ontology以及来自不同自然语言的定义和词汇的同义词集来描述相关领域的概念。用该方法构建的Ontology具有良好的扩展能力、表达能力和推理能力,特别适合分布式环境下大型Ontology的创建。