Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2009

Kern Roman, Granitzer Michael, Lindstaedt Stefanie , Ghidini C., Scheir Peter

ARS/SD: An Associative Retrieval Service for the Semantic Desktop

Networked Knowledge - Networked Media Integrating Knowledge Management, New Media Technologies and Semantic Systems, Studies in Computational Intelligence , Pellegrini, T., Auer, S., Tochtermann, K., Schaffert, S., Springer, 2009

Buch
While it is agreed that semantic enrichment of resources wouldlead to better search results, at present the low coverage of resources onthe web with semantic information presents a major hurdle in realizing thevision of search on the Semantic Web. To address this problem we investigatehow to improve retrieval performance in a setting where resources aresparsely annotated with semantic information. We suggest employing techniquesfrom associative information retrieval to find relevant material, whichwas not originally annotated with the concepts used in a query. We presentan associative retrieval service for the Semantic Desktop and evaluate if theuse of associative retrieval techniques increases retrieval performance.Evaluation of new retrieval paradigms, as retrieval in the Semantic Web oron the Semantic Desktop, presents an additional challenge as no off-the-shelftest corpora for evaluation exist. Hence we give a detailed description of the
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
2009

Zechner Mario, Granitzer Michael

K-Means on the Graphics Processor: Design And Experimental Analysis

International Journal on Advances in Systems and Measurements, Volume 2, Number 2&3, Paleologu, C., 2009

Journal
2009

Kern Roman, Juffinger A., Granitzer Michael

Application of Axiomatic Approaches to Crosslanguage Retrieval

Working Notes for the CLEF 2009 Workshop, 2009

Konferenz
2009

Kern Roman, Granitzer Michael

Efficient linear text segmentation based on information retrieval techniques

MEDES '09: Proceedings of the International Conference on Management of Emergent Digital EcoSystems, ACM, 2009

The task of linear text segmentation is to split a large text document into shorter fragments, usually blocks of consecutive sentences. The algorithms that demonstrated the best performance for this task come at the price of high computational complexity. In our work we present an algorithm that has a computational complexity of O(n) with n being the number of sentences in a document. The performance of our approach is evaluated against algorithms of higher complexity using standard benchmark data sets and we demonstrate that our approach provides comparable accuracy.
2009

Zechner Mario, Granitzer Michael

A Competitive Learning Approach to Instance Selection for Support Vector Machines

To appear in: Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management, 2009

Konferenz
2009

Shahzad S., Granitzer Michael

Designing User Interfaces through Ontological User Models

Proceedings of the Fourth International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2009, Seoul, Korea, IEEE Computer Society, 2009

Konferenz
2009

Granitzer Michael, Rath Andreas S., Kröll Mark, Ipsmiller D., Devaurs Didier, Weber Nicolas, Lindstaedt Stefanie , Seifert C.

Machine Learning based Work Task Classification

Journal of Digital Information Management, 2009

Journal
Increasing the productivity of a knowledgeworker via intelligent applications requires the identification ofa user’s current work task, i.e. the current work context a userresides in. In this work we present and evaluate machine learningbased work task detection methods. By viewing a work taskas sequence of digital interaction patterns of mouse clicks andkey strokes, we present (i) a methodology for recording thoseuser interactions and (ii) an in-depth analysis of supervised classificationmodels for classifying work tasks in two different scenarios:a task centric scenario and a user centric scenario. Weanalyze different supervised classification models, feature typesand feature selection methods on a laboratory as well as a realworld data set. Results show satisfiable accuracy and high useracceptance by using relatively simple types of features.
2009

Zechner Mario, Kern Roman, Granitzer Michael, Muhr M.

External and Intrinsic Plagiarism Detection Using Vector Space Models

Proceedings of the SEPLN'09 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, 2009

Konferenz
2009

Klieber Hans-Werner, Sabol Vedran, Granitzer Michael, Muhr M.

Using Ontologies For Software Documentation

Malaysian Joint Conference on Artificial Intelligence 2009, 2009

Konferenz
2009

Granitzer Michael, Malkom J., Ipsmiller D.

Wissensmanagement mit DYONIPOS: Konzepte, Algorithmen und Technologien

Tagungsband , Internationales Rechtsinformatik Symposion 2009 (to appear), Boorberg , 2009

2009

Neidhart T., Granitzer Michael, Kern Roman, Weichselbraun A., Wohlgenannt G., Scharl A., Juffinger A.

Distributed Web2.0 Crawling for Ontology Evolution

Journal of Digital Information Management, 2009

Journal
2009

Willfort R., Lex Elisabeth, Granitzer Michael, Juffinger A.

Spectral Web Content Trend Analysis

Proc. of IADIS International Conference WWW/Internet, 2009

Konferenz
2009

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

Cross-Domain Classification: Trade-Off between Complexity and Accuracy

Proceedings of the 4th International Conference for Internet Technology and Secured Transactions (ICITST) 2009, 2009

Text classification is one of the core applications in data mining due to the huge amount of not categorized digital data available. Training a text classifier generates a model that reflects the characteristics of the domain. However, if no training data is available, labeled data from a related but different domain might be exploited to perform crossdomain classification. In our work, we aim to accurately classify unlabeled blogs into commonly agreed newspaper categories using labeled data from the news domain. The labeled news and the unlabeled blog corpus are highly dynamic and hourly growing with a topic drift, so a trade-off between accuracy and performance is required. Our approach is to apply a fast novel centroid-based algorithm, the Class-Feature-Centroid Classifier (CFC), to perform efficient cross-domain classification. Experiments showed that this algorithm achieves a comparable accuracy than k-NN and is slightly better than Support Vector Machines (SVM), yet at linear time cost for training and classification. The benefit of this approach is that the linear time complexity enables us to efficiently generate an accurate classifier, reflecting the topic drift, several times per day on a huge dataset.
2009

Muhr M., Granitzer Michael

Automatic Cluster Number Selection using a Split and Merge K-Means Approach

6th International Workshop on Text-based Information Retrieval in Procceedings of 20th International Conference on Database and Expert Systems Applications (DEXA 09), IEEE Computer Society, 2009

Konferenz
2009

Zechner Mario, Granitzer Michael

Accelerating K-Means on the Graphics Processor via CUDA

Proceedings of the 2009 First International Conference on Intensive Applications and Services (INTENSIVE 2009), IEEE Computer Society, 2009

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

Sabol Vedran, Kienreich Wolfgang, Klieber Hans-Werner, Granitzer Michael, Muhr M.

Visual Knowledge Discovery in Dynamic Enterprise Text Repositories

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

Konferenz
2009

Granitzer Michael, Stocker A.

Can Intra-Organizational Wikis Facilitate Knowledge Transfer and Learning? An Explorative Case Study

Proceedings of eLBa - eLearning Baltics 2009, 2009

Konferenz
2009

Klieber Hans-Werner, Sabol Vedran, Kern Roman, Granitzer Michael, Muhr M., Ättl G.

Knowledge Discovery Using the Knowminer Framework

IADIS International Conference Information Systems 2009, 2009

Konferenz
2009

Granitzer Michael, Lex Elisabeth, Juffinger A.

Blog Credibility Ranking by Exploiting Verified Content

Proceedings of the 3rd Workshop on Information Credibility on the Web at 18th World Wide Web Conference, 2009

Konferenz
People use weblogs to express thoughts, present ideas and share knowledge. However, weblogs can also be misused to influence and manipulate the readers. Therefore the credibility of a blog has to be validated before the available information is used for analysis. The credibility of a blogentry is derived from the content, the credibility of the author or blog itself, respectively, and the external references or trackbacks. In this work we introduce an additional dimension to assess the credibility, namely the quantity structure. For our blog analysis system we derive the credibility therefore from two dimensions. Firstly, the quantity structure of a set of blogs and a reference corpus is compared and secondly, we analyse each separate blog content and examine the similarity with a verified news corpus. From the content similarity values we derive a ranking function. Our evaluation showed that one can sort out incredible blogs by quantity structure without deeper analysis. Besides, the content based ranking function sorts the blogs by credibility with high accuracy. Our blog analysis system is therefore capable of providing credibility levels per blog.
2009

Lex Elisabeth, Granitzer Michael, Juffinger A.

Know-Center at TREC 2009 Blog Distillation Task: A Notebook Paper

Notebook of TREC 2009, 2009

Konferenz
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