Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2011

Helic Denis, Strohmaier M., Trattner Christoph, Muhr M., Lerman K.

Pragmatic Evaluation of Folksonomies

20th International World Wide Web Conference (WWW2011), 2011

Konferenz
2010

Kern Roman, Granitzer Michael, Muhr M.

KCDC: Word Sense Induction by Using Grammatical Dependencies and Sentence Phrase Structure

Proceedings of SemEval-2, 2010

Konferenz
Word sense induction and discrimination(WSID) identifies the senses of an ambiguousword and assigns instances of thisword to one of these senses. We have builda WSID system that exploits syntactic andsemantic features based on the results ofa natural language parser component. Toachieve high robustness and good generalizationcapabilities, we designed our systemto work on a restricted, but grammaticallyrich set of features. Based on theresults of the evaluations our system providesa promising performance and robustness.
2010

Kern Roman, Granitzer Michael, Muhr M.

Analysis of Structural Relationships for Hierarchical Cluster Labeling

Proceeding of the 33rd international ACM SIGIR Conference on Research and Development in information Retrieval, ACM, 2010

Konferenz
Cluster label quality is crucial for browsing topic hierarchiesobtained via document clustering. Intuitively, the hierarchicalstructure should influence the labeling accuracy. However,most labeling algorithms ignore such structural propertiesand therefore, the impact of hierarchical structureson the labeling accuracy is yet unclear. In our work weintegrate hierarchical information, i.e. sibling and parentchildrelations, in the cluster labeling process. We adaptstandard labeling approaches, namely Maximum Term Frequency,Jensen-Shannon Divergence, χ2 Test, and InformationGain, to take use of those relationships and evaluatetheir impact on 4 different datasets, namely the Open DirectoryProject, Wikipedia, TREC Ohsumed and the CLEFIP European Patent dataset. We show, that hierarchicalrelationships can be exploited to increase labeling accuracyespecially on high-level nodes.
2010

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

External and Intrinsic Plagiarism Detection using a Cross-Lingual Retrieval and Segmentation System Lab Report for PAN at CLEF 2010

2nd International Competition on Plagiarism Detection, 2010

Konferenz
We present our hybrid system for the PAN challenge at CLEF 2010.Our system performs plagiarism detection for translated and non-translated externallyas well as intrinsically plagiarized document passages. Our external plagiarismdetection approach is formulated as an information retrieval problem, usingheuristic post processing to arrive at the final detection results. For the retrievalstep, source documents are split into overlapping blocks which are indexed via aLucene instance. Suspicious documents are similarly split into consecutive overlappingboolean queries which are performed on the Lucene index to retrieve aninitial set of potentially plagiarized passages. For performance reasons queriesmight get rejected via a heuristic before actually being executed. Candidate hitsgathered via the retrieval step are further post-processed by performing sequenceanalysis on the passages retrieved from the index with respect to the passagesused for querying the index. By applying several merge heuristics bigger blocksare formed from matching sequences. German and Spanish source documentsare first translated using word alignment on the Europarl corpus before enteringthe above detection process. For each word in a translated document severaltranslations are produced. Intrinsic plagiarism detection is done by finding majorchanges in style measured via word suffixes after the documents have been partitionedby an linear text segmentation algorithm. Our approach lead us to the thirdoverall rank with an overall score of 0.6948.
2010

Sabol Vedran, Syed K. A. A., Scharl A., Hubmann-Haidvogel A., Muhr M.

Incremental Computation of Information Landscapes for Dynamic Web Interfaces

Proceedings of the 10th Brazilian Symposium on Human Factors in Computer Systems , 2010

Konferenz
2010

Sabol Vedran, Granitzer Michael, Muhr M.

Scalable Recursive Top-Down Hierarchical Clustering Approach with implicit Model Selection for Textual Data Sets

IEEE Computer Society: 7th International Workshop on Text-based Information Retrieval in Procceedings of 21th International Conference on Database and Expert Systems Applications (DEXA 10)., IEEE, 2010

Konferenz
Automatic generation of taxonomies can be usefulfor a wide area of applications. In our application scenario atopical hierarchy should be constructed reasonably fast froma large document collection to aid browsing of the data set.The hierarchy should also be used by the InfoSky projectionalgorithm to create an information landscape visualizationsuitable for explorative navigation of the data. We developedan algorithm that applies a scalable, recursive, top-downclustering approach to generate a dynamic concept hierarchy.The algorithm recursively applies a workflow consisting ofpreprocessing, clustering, cluster labeling and projection into2D space. Besides presenting and discussing the benefits ofcombining hierarchy browsing with visual exploration, we alsoinvestigate the clustering results achieved on a real world dataset.
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

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

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

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

Using Ontologies For Software Documentation

Malaysian Joint Conference on Artificial Intelligence 2009, 2009

Konferenz
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
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