A Semantics-Based, End-User-Centered Information Visualization Process for Semantic Web Data

Author(s):  
Martin Voigt ◽  
Stefan Pietschmann ◽  
Klaus Meißner
2009 ◽  
Vol 20 (11) ◽  
pp. 2950-2964 ◽  
Author(s):  
Xiao-Yong DU ◽  
Yan WANG ◽  
Bin LÜ

Author(s):  
Matthew Perry ◽  
Amit P. Sheth ◽  
Farshad Hakimpour ◽  
Prateek Jain
Keyword(s):  

Author(s):  
Georg Neubauer

The main subject of the work is the visualization of typed links in Linked Data. The academic subjects relevant to the paper in general are the Semantic Web, the Web of Data and information visualization. The Semantic Web, invented by Tim Berners-Lee in 2001, was announced as an extension to the World Wide Web (Web 2.0). The actual area of investigation concerns the connectivity of information on the World Wide Web. To be able to explore such interconnections, visualizations are critical requirements as well as a major part of processing data in themselves. In the context of the Semantic Web, representation of information interrelations can be achieved using graphs. The aim of the article is to primarily describe the arrangement of Linked Data visualization concepts by establishing their principles in a theoretical approach. Putting design restrictions into context leads to practical guidelines. By describing the creation of two alternative visualizations of a commonly used web application representing Linked Data as network visualization, their compatibility was tested. The application-oriented part treats the design phase, its results, and future requirements of the project that can be derived from this test.


Author(s):  
Jiaoyan Chen ◽  
Freddy Lecue ◽  
Jeff Z. Pan ◽  
Huajun Chen

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.


2006 ◽  
pp. 19-44 ◽  
Author(s):  
Lawrence Reeve ◽  
Hyoil Han ◽  
Chaomei Chen

Author(s):  
Giorgio Gianforme ◽  
Roberto De Virgilio ◽  
Stefano Paolozzi ◽  
Pierluigi Del Nostro ◽  
Danilo Avola

Author(s):  
Markus Kirchberg ◽  
Erwin Leonardi ◽  
Yu Shyang Tan ◽  
Sebastian Link ◽  
Ryan K. L. Ko ◽  
...  

Author(s):  
Juan Li ◽  
Ranjana Sharma ◽  
Yan Bai

Drug discovery is a lengthy, expensive and difficult process. Indentifying and understanding the hidden relationships among drugs, genes, proteins, and diseases will expedite the process of drug discovery. In this paper, we propose an effective methodology to discover drug-related semantic relationships over large-scale distributed web data in medicine, pharmacology and biotechnology. By utilizing semantic web and distributed system technologies, we developed a novel hierarchical knowledge abstraction and an efficient relation discovery protocol. Our approach effectively facilitates the realization of the full potential of harnessing the collective power and utilization of the drug-related knowledge scattered over the Internet.


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