Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2014

Granitzer MIchael, Veas Eduardo Enrique, Seifert C.

Linked Data Query Wizard: A Novel Interface for Accessing SPARQL Endpoints.

LDOW, 2014

Konferenz
In an interconnected world, Linked Data is more importantthan ever before. However, it is still quite di cult to accessthis new wealth of semantic data directly without havingin-depth knowledge about SPARQL and related semantictechnologies. Also, most people are currently used to consumingdata as 2-dimensional tables. Linked Data is by de -nition always a graph, and not that many people are used tohandle data in graph structures. Therefore we present theLinked Data Query Wizard, a web-based tool for displaying,accessing, ltering, exploring, and navigating Linked Datastored in SPARQL endpoints. The main innovation of theinterface is that it turns the graph structure of Linked Datainto a tabular interface and provides easy-to-use interactionpossibilities by using metaphors and techniques from currentsearch engines and spreadsheet applications that regular webusers are already familiar with.
2011

Seifert Christin, Ulbrich Eva Pauline, Granitzer Michael

Word Clouds for Efficient Document Labeling

The Fourteenth International Conference on Discovery Science (DS 2011), Lecture Notes in Computer Science, Springer, 2011

Konferenz
In text classification the amount and quality of training datais crucial for the performance of the classifier. The generation of trainingdata is done by human labelers - a tedious and time-consuming work. Wepropose to use condensed representations of text documents instead ofthe full-text document to reduce the labeling time for single documents.These condensed representations are key sentences and key phrases andcan be generated in a fully unsupervised way. The key phrases are presentedin a layout similar to a tag cloud. In a user study with 37 participantswe evaluated whether document labeling with these condensedrepresentations can be done faster and equally accurate by the humanlabelers. Our evaluation shows that the users labeled word clouds twiceas fast but as accurately as full-text documents. While further investigationsfor different classification tasks are necessary, this insight couldpotentially reduce costs for the labeling process of text documents.
2010

Sabol Vedran, Kienreich Wolfgang, Seifert C.

Stress Maps: Analysing Local Phenomena in Dimensionality Reduction Based Visualizations

European Symposium Visual Analytics Science and Technology (EuroVAST), 2010

Konferenz
2010

Sabol Vedran, Kienreich Wolfgang, Seifert C.

Integrating Node-Link-Diagrams and Information Landscapes: A Path-Finding Approach

Poster and Demo at EuroVis 2010, 2010

Konferenz
2010

Sabol Vedran, Granitzer Michael, Seifert C.

Classifier Hypothesis Generation Using Visual Analysis Methods

NDT: Networked Digital Technologies, Springer, 2010

Konferenz
Classifiers can be used to automatically dispatch the abundanceof newly created documents to recipients interested in particulartopics. Identification of adequate training examples is essential forclassification performance, but it may prove to be a challenging task inlarge document repositories. We propose a classifier hypothesis generationmethod relying on automated analysis and information visualisation.In our approach visualisations are used to explore the document sets andto inspect the results of machine learning methods, allowing the user toassess the classifier performance and adapt the classifier by graduallyrefining the training set.
2010

Kienreich Wolfgang, Seifert C.

An Application of Edge Bundling Techniques to the Visualization of Media Analysis Results

IV2010: International Conference on Information Visualization, IEEE Computer Society Press, 2010

Konferenz
The advent of consumer-generated and socialmedia has led to a continuous expansion and diversificationof the media landscape. Media consumers frequently findthemselves assuming the role of media analysts in order tosatisfy personal information needs. We propose to employKnowledge Visualization methods in support of complex mediaanalysis tasks. In this paper, we describe an approach whichdepicts semantic relationships between key political actorsusing node-link diagrams. Our contribution comprises a forcedirectededge bundling algorithm which accounts for semanticproperties of edges, a technical evaluation of the algorithmand a report on a real-world application of the approach. Theresulting visualization fosters the identification of high-leveledge patterns which indicate strong semantic relationships. Ithas been published by the Austrian Press Agency APA in 2009.
2010

Seifert C., Granitzer Michael

User-based active learning

International Conference on Data Mining Workshops (Workshop on Visual Analytics and Knowledge Discovery), Fan, W., Hsu, W.,Webb, G. I., Liu, B., Zhang, C., Gunopulos, D., Wu, X., IEEE, 2010

Konferenz
Active learning has been proven a reliable strategyto reduce manual efforts in training data labeling. Suchstrategies incorporate the user as oracle: the classifier selectsthe most appropriate example and the user provides the label.While this approach is tailored towards the classifier, moreintelligent input from the user may be beneficial. For instance,given only one example at a time users are hardly ableto determine whether this example is an outlier or not. Inthis paper we propose user-based visually-supported activelearning strategies that allow the user to do both, selectingand labeling examples given a trained classifier. While labelingis straightforward, selection takes place using a interactivevisualization of the classifier’s a-posteriori output probabilities.By simulating different user selection strategies we show,that user-based active learning outperforms uncertainty basedsampling methods and yields a more robust approach ondifferent data sets. The obtained results point towards thepotential of combining active learning strategies with resultsfrom the field of information visualization.
2009

Granitzer Michael, Zechner Mario, Seifert C.

Context based Wikipedia Linking

Advances in Focused Retrieval 7th International Workshop of the Initiative for the Evaluation of XML Retrieval (INEX 2008), Geva, S., Kamps, J., Trotman, A., Springer, 2009

Konferenz
2009

Lex Elisabeth, Seifert C.

A Novel Visualization Approach for Data-Mining-Related Classi?cation

Proceedings of the 13th International Conference on Information Visualisation (IV09), IEEE Computer Society, 2009

Konferenz
2009

Lex Elisabeth, Seifert C.

A Visualization to Investigate and Give Feedback to Classifiers

Poster and Demo at the EuroVis 2009, 2009

Konferenz
2009

Lex Elisabeth, Granitzer Michael, Juffinger A., Seifert C.

Automated Blog Classification: A Cross Domain Approach

Proc. of IADIS International Conference WWW/Internet, 2009

Konferenz
2008

Kienreich Wolfgang, Lex Elisabeth, Seifert C.

APA Labs: An Experimental Web-Based Platform for the Retrieval and Analysis of News Articles

Proceedings of the first International Conference on the Applications and Digital Information and Web Technologies (ICADIWT08), 2008

Konferenz
2008

Granitzer Michael, Seifert C., Zechner Mario

Context Resolution Strategies for Automatic Wikipedia Linking

INEX 2008 pre-proceedings, Dagstuhl, Germany, Geva, S., Kamps, J., Trotman, A., Shlomo Geva and Jaap Kamps and Andrew Trotman (Eds.), 2008

Konferenz
2008

Kump Barbara, Kienreich Wolfgang, Granitzer Gisela, Granitzer Michael, Seifert C.

On the beauty and usability of tag clouds

Proceedings of the 12 International Conference on Information Visualization (IV2008), London, UK, July 9-11, 2008, IEEE Computer Society Press, 2008

Konferenz
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