Scalable Knowledge Extraction and Visualization for Web Intelligence: 2016 49th Hawaii International Conference on System Sciences (HICSS)

A. Scharl; A. Weichselbraun; M. Göbel; W. Rafelsberger; R. Kamolov

doi:10.1109/HICSS.2016.467

Scalable Knowledge Extraction and Visualization for Web Intelligence: 2016 49th Hawaii International Conference on System Sciences (HICSS)

A. Scharl, A. Weichselbraun, M. Göbel, W. Rafelsberger, R. Kamolov

Research output: Contribution to conference › Other › Research › peer-review

Abstract

Understanding stakeholder perceptions and assessing the impact of campaigns are key questions of communication experts. Web intelligence platforms help to answer such questions, provided that they are scalable enough to analyze and visualize information flows from volatile online sources in real time. This paper presents a distributed architecture for aggregating Web content repositories from Web sites and social media streams, memory-efficient methods to extract factual and affective knowledge, and interactive visualization techniques to explore the extracted knowledge. The presented examples stem from the Media Watch on Climate Change, a public Web portal that aggregates environmental content from a range of online sources.

Original language	English
Pages	3749-3757
Number of pages	9
DOIs	https://doi.org/10.1109/HICSS.2016.467
Publication status	Published - 2016

Keywords

data visualisation
knowledge acquisition
social networking (online)
Web Intelligence
Web content repository
Web sites
distributed architecture
interactive visualization technique
knowledge visualization
memory-efficient method
scalable knowledge extraction
social media stream
Companies
Knowledge engineering
Market research
Media
Meteorology
Real-time systems
Visualization
Web intelligence
knowledge extraction
science communication
visual analytics

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10.1109/HICSS.2016.467

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title = "Scalable Knowledge Extraction and Visualization for Web Intelligence: 2016 49th Hawaii International Conference on System Sciences (HICSS)",

abstract = "Understanding stakeholder perceptions and assessing the impact of campaigns are key questions of communication experts. Web intelligence platforms help to answer such questions, provided that they are scalable enough to analyze and visualize information flows from volatile online sources in real time. This paper presents a distributed architecture for aggregating Web content repositories from Web sites and social media streams, memory-efficient methods to extract factual and affective knowledge, and interactive visualization techniques to explore the extracted knowledge. The presented examples stem from the Media Watch on Climate Change, a public Web portal that aggregates environmental content from a range of online sources.",

keywords = "data visualisation, knowledge acquisition, social networking (online), Web Intelligence, Web content repository, Web sites, distributed architecture, interactive visualization technique, knowledge visualization, memory-efficient method, scalable knowledge extraction, social media stream, Companies, Knowledge engineering, Market research, Media, Meteorology, Real-time systems, Visualization, Web intelligence, knowledge extraction, science communication, visual analytics",

author = "A. Scharl and A. Weichselbraun and M. G{\"o}bel and W. Rafelsberger and R. Kamolov",

year = "2016",

doi = "10.1109/HICSS.2016.467",

language = "English",

pages = "3749--3757",

}

TY - CONF

T1 - Scalable Knowledge Extraction and Visualization for Web Intelligence

T2 - 2016 49th Hawaii International Conference on System Sciences (HICSS)

AU - Scharl, A.

AU - Weichselbraun, A.

AU - Göbel, M.

AU - Rafelsberger, W.

AU - Kamolov, R.

PY - 2016

Y1 - 2016

N2 - Understanding stakeholder perceptions and assessing the impact of campaigns are key questions of communication experts. Web intelligence platforms help to answer such questions, provided that they are scalable enough to analyze and visualize information flows from volatile online sources in real time. This paper presents a distributed architecture for aggregating Web content repositories from Web sites and social media streams, memory-efficient methods to extract factual and affective knowledge, and interactive visualization techniques to explore the extracted knowledge. The presented examples stem from the Media Watch on Climate Change, a public Web portal that aggregates environmental content from a range of online sources.

AB - Understanding stakeholder perceptions and assessing the impact of campaigns are key questions of communication experts. Web intelligence platforms help to answer such questions, provided that they are scalable enough to analyze and visualize information flows from volatile online sources in real time. This paper presents a distributed architecture for aggregating Web content repositories from Web sites and social media streams, memory-efficient methods to extract factual and affective knowledge, and interactive visualization techniques to explore the extracted knowledge. The presented examples stem from the Media Watch on Climate Change, a public Web portal that aggregates environmental content from a range of online sources.

KW - data visualisation

KW - knowledge acquisition

KW - social networking (online)

KW - Web Intelligence

KW - Web content repository

KW - Web sites

KW - distributed architecture

KW - interactive visualization technique

KW - knowledge visualization

KW - memory-efficient method

KW - scalable knowledge extraction

KW - social media stream

KW - Companies

KW - Knowledge engineering

KW - Market research

KW - Media

KW - Meteorology

KW - Real-time systems

KW - Visualization

KW - Web intelligence

KW - knowledge extraction

KW - science communication

KW - visual analytics

U2 - 10.1109/HICSS.2016.467

DO - 10.1109/HICSS.2016.467

M3 - Other

SP - 3749

EP - 3757

ER -

Scalable Knowledge Extraction and Visualization for Web Intelligence: 2016 49th Hawaii International Conference on System Sciences (HICSS)

Abstract

Keywords

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