Abstract

Due to the overwhelming amount of news articles from a growing number of sources, it has become nearly impossible for humans to select and read all articles that are relevant to get deep insights and form conclusions. This leads to a need for an easy way to aggregate and analyze news articles efficiently and visualize the garnered knowledge as a base for further cognitive processing. The presented application provides a tool to satisfy said need. In our approach we use semantic techniques to extract named entities, relations and locations from different news sources in different languages. This knowledge is used as the base for multiple data aggregation and visualization operators. The data operators include filtering of entities, types and date range, detection of correlated news topics for a set of selected entities and geospacial analysis based on locations. Our visualization provides a time-based graphical representation of news mentions according to the given filters as well as an interactive map which displays news within a perimeter for the different locations mentioned in the news articles. In every step of the user process, we offer a tag cloud that highlights popular results and provides links to the original sources including highlighted snippets. Using the graphical interface, the user is able to analyze and explore vast amounts of fresh news articles, find possible relations and perform trend analysis in an intuitive way.

@inproceedings{ABDLL:NewsVisualizationBasedOnSemanticKnowledge,
author = {Sebastian Arnold and Damian Burke and Tobias D{"o}rsch and Bernd L{"o}ber and Andreas Lommatzsch},
title = {News Visualization based on Semantic Knowledge},
booktitle = {Proceedings of the 13th ISWC conference 2014},
year = {2014},
location = {Riva del Garda, Trentino, Italy},
numpages = {4}
pages = {5--8},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-1272/},
note ={urn:nbn:de:0074-1272-7}
}