Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2011

Granitzer Michael, Lindstaedt Stefanie

Web 2.0: Applications and Mechanisms J.UCS Special Issue

JUCS - Journal of Universal Computing, 2011

Journal
2011

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

Konferenz
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.
2011

Granitzer Michael, Kienreich Wolfgang, Seifert Christin

Visualizing Text Classification Models with Voronoi Word Clouds

Proceedings 15th International Conference Information Visualisation (IV), 2011

Journal
2011

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

Konferenz
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.
2011

Granitzer Michael, Lindstaedt Stefanie

Semantic Web: Theory and Applications

Journal of Universal Computer Science, 2011

Journal
2011

Granitzer Michael, Lindstaedt Stefanie

Knowledge Work : Knowledge Worker Productivity , Collaboration and User Support

J.UCS - Journal of Universal Computer Science, 2011

Journal
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.
2011

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

Konferenz
2011

Kern Roman, Seifert Christin, Zechner Mario, Granitzer Michael

Vote/Veto Meta-Classifier for Authorship Identification

CLEF 2011: Proceedings of the 2011 Conference on Multilingual and Multimodal Information Access Evaluation (Lab and Workshop Notebook Papers), Amsterdam, The Netherlands, 2011

For the PAN 2011 authorship identification challenge we have developeda system based on a meta-classifier which selectively uses the results ofmultiple base classifiers. In addition we also performed feature engineering basedon the given domain of e-mails. We present our system as well as results on theevaluation dataset. Our system performed second and third best in the authorshipattribution task on the large data sets, and ranked middle for the small data set inthe attribution task and in the verification task.
2011

Kow Weng Onn, Sabol Vedran, Granitzer Michael, Kienreich Wolfgang, Lukose Dickson

A Visual SOA-based Ontology Alignment Tool

in Proceedings of the Sixth International Workshop on Ontology Matching (OM-2011), CEUR-WS. org, 2011

Journal
Ontology alignment is the process of matching related concepts fromdifferent ontologies. We propose a semi-automatic, visual approach whichcombines two algorithms for finding candidate alignments with visualnavigation and analysis tools. The implementation is based on a ServiceOrientedArchitecture (SOA) to achieve scalability.
2011

Horn Christopher, Pimas Oliver, Granitzer Michael, Lex Elisabeth

Realtime Ad Hoc Search in Twitter: Know-Center at TREC Microblog Track 2011

Proceedings of TREC 2011, 2011

In this paper, we outline our experiments carried out at theTREC Microblog Track 2011. Our system is based on a plain text indexextracted from Tweets crawled from twitter.com. This index hasbeen used to retrieve candidate Tweets for the given topics. The resultingTweets were post-processed and then analyzed using three differentapproaches: (i) a burst detection approach, (ii) a hashtag analysis, and(iii) a Retweet analysis. Our experiments consisted of four runs: Firstly,a combination of the Lucene ranking with the burst detection, and secondly,a combination of the Lucene ranking, the burst detection, and thehashtag analysis. Thirdly, a combination of the Lucene ranking, the burstdetection, the hashtag analysis, and the Retweet analysis, and fourthly,again a combination of the Lucene ranking with the burst detection butin this case with more sophisticated query language and post-processing.We achieved the best MAP values overall in the fourth run.
2011

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

Konferenz
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.
2011

Granitzer Michael, Tochtermann Klaus

Future Internet and the Library World

ZEITSCHRIFT FUR BIBLIOTHEKSWESEN UND BIBLIOGRAPHIE, 2011

Journal
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