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Shahzad Syed K, Granitzer Michael, Helic Denis

Ontological Model Driven GUI Development: User Interface Ontology Approach

6th International Conference on Computer Sciences and Convergence Information Technology (ICCIT), IEEE, 2011

Ontology and Semantic Framework has becomepervasive in computer science. It has huge impact at database,business logic and user interface for a range of computerapplications. This framework is also being introduced, presentedor plugged at user interfaces for various software and websites.However, establishment of structured and standardizedontological model based user interface development environmentis still a challenge. This paper talks about the necessity of such anenvironment based on User Interface Ontology (UIO). To explorethis phenomenon, this research focuses at the User Interfaceentities, their semantics, uses and relationships among them. Thefirst part focuses on the development of User Interface Ontology.In the second step, this ontology is mapped to the domainontology to construct a User Interface Model. Finally, theresulting model is quantified and instantiated for a user interfacedevelopment to support our framework. This UIO is anextendable framework that allows defining new sub-conceptswith their ontological relationships and constraints.

Declerck Thierry, Granitzer Michael, Grzegorzek Marcin, Romanelli Massimo, Rüger Stefan, Sintek Michael

Semantic Multimedia - 5th International Conference on Semantic and Digital Media Technologies, SAMT 2010

Lecture Notes in Computer Science, Vol. 6725, Declerck, T.; Granitzer, M.; Grzegorzek, M.; Romanelli, M.; Rüger, S.; Sintek, M., Springer, 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

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.

Horn Christopher, Lex Elisabeth, Granitzer Michael

Who Tweets: Detecting User Types and Tweet Quality using Supervised Classification

IADIS Multiconference on Computer Science and Information Systems, 2011

Social networking tools like Twitter are the latest trend in the global world. However, due to the increasing amount ofcontent in Twitter, there is a need for information filtering by facets like user type and content quality. In this work, weaddress this challenge by classifying users into three user types, "news", "personal user", and "advertisements".Additionally, we assess the quality of the Tweets by classifying them into "factual" versus "opinionated". We evaluatedword stemming and regular expressions as data pre-processing techniques and found that with simple regularexpressions, a sound classification accuracy of more than 80% can be achieved. Besides, we propose a web-based TwitterClassification Application that enables to manually annotate Tweets into a set of pre-defined classes with maintainableeffort.

Kern Roman, Zechner Mario, Granitzer Michael

Model Selection Strategies for Author Disambiguation

IEEE Computer Society: 8th International Workshop on Text-based Information Retrieval in Procceedings of 22th International Conference on Database and Expert Systems Applications (DEXA 11), IEEE , 2011

Author disambiguation is a prerequisite for utilizingbibliographic metadata in citation analysis. Automaticdisambiguation algorithms mostly rely on cluster-based disambiguationstrategies for identifying unique authors given theirnames and publications. However, most approaches rely onknowing the correct number of unique authors a-priori, whichis rarely the case in real world settings. In this publicationwe analyse cluster-based disambiguation strategies and developa model selection method to estimate the number of distinctauthors based on co-authorship networks. We show that, givenclean textual features, the developed model selection methodprovides accurate guesses of the number of unique authors.
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