Central aim of organisation
webLyzard provides research and consulting services with a focus on media monitoring, Web mining and semantic technologies
Briefly describe your organisation
webLyzard technology gmbh is a technology SME founded in 2008 that pursues research and development in Web intelligence and visual analytics, drawing on the extensive expertise of its founders in the fields of Web mining, the Geospatial Web, information visualization, human-computer interaction, knowledge co-creation and new forms of communication and collaboration. Current clients of webLyzard include the United Nations Environment Programme (UNEP), the U.S. Department of Commerce (NOAA Climate.gov), and a number of large Business-to-Consumer brands. To support decision making processes within these organizations, webLyzard identifies relevance and sentiment of online content, measures brand reputation, and provides the industry’s most advanced communication success metric.
Research & Development
|- ICT Applications |
- Knowledge Management
- Archiving, Documentation
- Information and Multimedia Retrieval
- Semantic Systems
| - Internet Technologies |
- Cognitive Systems, Interaction and Robotics
- Man-Machine Interaction
- Artificial Intelligence
- Data Mining
media monitoring, web mining, web assessment, semantic technology, textual statistics, visual analytics, information retrieval
My organisation has been involved in projects funded by the following EU-programmes
Austrian Research and Development Projects
webLyzard’s award-winning big data platform builds on more than 15 years of focused R&D into text mining, natural language processing, linked data, human-computer interaction, and information visualization. To support decision makers, the highly scalable platform detects emerging stories, visualizes semantic associations and provides one of the industry’s most advanced communication success metrics. webLyzard has a strong record in acquiring and managing large-scale research projects, including both national projects and European initiatives (FP7, Horizon 2020).
Scharl, A., Herring, D., Rafelsberger, W., Hubmann-Haidvogel, A., Kamolov, R., Fischl, D., Föls, M. and Weichselbraun, A. (2017). “Semantic Systems and Visual Tools to Support Environmental Communication”, IEEE Systems Journal, 11(2): 762-771.
Weichselbraun, A., Gindl, S., Fischer, F., Vakulenko, S. and Scharl, A. (2017). “Aspect-Based Extraction and Analysis of Affective Knowledge from Social Media Stream”, IEEE Intelligent Systems, 32(3): 80-88.
Brasoveanu, A.M.P., Sabou, M., Scharl, A., Hubmann-Haidvogel, A. and Fischl, D. (2017). “Visualizing Statistical Linked Knowledge for Decision Support”, Semantic Web Journal, 8(1): 113-137.
Weichselbraun, A., Streiff, D. and Scharl, A. (2015). “Consolidating Heterogeneous Enterprise Data for Named Entity Linking and Web Intelligence”, International Journal on Artificial Intelligence Tools, 24(2): 1540008 | 1-31.
Weichselbraun, A., Gindl, S. and Scharl, A. (2014). “Enriching Semantic Knowledge Bases for Opinion Mining in Big Data Applications”, Knowledge-Based Systems, 69: 78-86.
Scharl, A., Hubmann-Haidvogel, A., et al. (2013). “From Web Intelligence to Knowledge Co-Creation – A Platform to Analyze and Support Stakeholder Communication”, IEEE Internet Computing, 17(5): 21-29.
Weichselbraun, A., Gindl, S. and Scharl, A. (2013). “Extracting and Grounding Contextualized Sentiment Lexicons”, IEEE Intelligent Systems, 28(2): 39-46.
Inserted / Updated
2008-11-16 / 2018-06-08