首页> 外文期刊>The VLDB journal >ViSWeb - the Visual Semantic Web: unifying human and machine knowledge representations with Object-Process Methodology
【24h】

ViSWeb - the Visual Semantic Web: unifying human and machine knowledge representations with Object-Process Methodology

机译:ViSWeb-视觉语义网:使用对象处理方法统一人机知识

获取原文
获取原文并翻译 | 示例
           

摘要

The Visual Semantic Web (ViSWeb) is a new paradigm for enhancing the current Semantic Web technology. Based on Object-Process Methodology (OPM), which enables modeling of systems in a single graphic and textual model, ViSWeb provides for representation of knowledge over the Web in a unified way that caters to human perceptions while also being machine processable. The advantages of the ViSWeb approach include equivalent graphic-text knowledge representation, visual navigability, semantic sentence interpretation, specification of system dynamics, and complexity management. Arguing against the claim that humans and machines need to look at different knowledge representation formats, the principles and basics of various graphic and textual knowledge representations are presented and examined as candidates for ViSWeb foundation. Since OPM is shown to be most adequate for the task, ViSWeb is developed as an OPM-based layer on top of XML/RDF/OWL to express knowledge visually and in natural language. Both the graphic and the textual representations are strictly equivalent. Being intuitive yet formal, they are not only understandable to humans but are also amenable to computer processing. The ability to use such bimodal knowledge representation is potentially a major step forward in the evolution of the Semantic Web.
机译:视觉语义网(ViSWeb)是用于增强当前语义网技术的新范例。基于对象处理方法论(OPM),该方法可以在单个图形和文本模型中对系统进行建模,ViSWeb以统一的方式提供Web上的知识表示,既可以满足人类的感知,又可以进行机器处理。 ViSWeb方法的优点包括等效的图形文本知识表示,可视化导航,语义句子解释,系统动力学规范和复杂性管理。关于人和机器需要查看不同的知识表示格式的主张,提出了各种图形和文本知识表示的原理和基础,并作为ViSWeb基础的候选者进行了研究。由于显示OPM最适合完成该任务,因此ViSWeb被开发为XML / RDF / OWL之上基于OPM的层,以视觉和自然语言表达知识。图形和文字表示均严格等效。它们既直观却又形式化,不仅为人类所理解,而且还可以进行计算机处理。使用这种双峰知识表示的能力可能是语义网发展中的重要一步。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号