News

Dominique Ritze defended her PhD Thesis

On November 6th, Dominique Ritze successfully defended her PhD thesis Web-Scale Web Table to Knowledge Base Matching. Supervisor was Prof. Christian Bizer, second reader was Prof. Kai Eckert from Hochschule der Medien Stattgart. 

Abstract of the thesis:

Millions of relational HTML tables are found on the World Wide Web. In contrast to unstructured text, relational web tables provide a compact representation of entities described by attributes. The data within these tables covers a broad topical range. Web table data is used for question answering, augmentation of search results, and knowledge base completion. Until a few years ago, only search engines companies like Google and Microsoft owned large web crawls from which web tables are extracted. Thus, researches outside the companies have not been able to work with web tables.

In this thesis, the first publicly available web table corpus containing millions of web tables is introduced. The corpus enables interested researchers to experiment with web tables. A profile of the corpus is created to give insights to the characteristics and topics. Further, the potential of web tables for augmenting cross-domain knowledge bases is investigated. For the use case of knowledge base augmentation, it is necessary to understand the web table content. For this reason, web tables are matched to a knowledge base. The matching comprises three matching tasks: instance, property, and class matching. Existing web table to knowledge base matching systems either focus on a subset of these matching tasks or are evaluated using gold standards which also only cover a subset of the challenges that arise when matching web tables to knowledge bases.

This thesis systematically evaluates the utility of a wide range of different features for the web table to knowledge base matching task using a single gold standard. The results of the evaluation are used afterwards to design a holistic matching method which covers all matching tasks and outperforms state-of-the-art web table to knowledge base matching systems. In order to achieve these goals, we first propose the T2K Match algorithm which addresses all three matching tasks in an integrated fashion. In addition, we introduce the T2D gold standard which covers a wide variety of challenges. By evaluating T2K Match against the T2D gold standard, we identify that only considering the table content is insufficient. Hence, we include features of three categories: features found in the table, in the table context like the page title, and features that base on external resources like a synonym dictionary.

We analyze the utility of the features for each matching task. The analysis shows that certain problems cannot be overcome by matching each table in isolation to the knowledge base. In addition, relying on the features is not enough for the property matching task. Based on these findings, we extend T2K Match into T2K Match++ which exploits indirect matches to web tables about the same topic and uses knowledge derived from the knowledge base. We show that T2K Match++ outperforms all state-of-the-art web table to knowledge base matching approaches on the T2D and Limaye gold standard. Most systems show good results on one matching task but T2K Match++ is the only system that achieves F-measure scores above 0:8 for all tasks. Compared to results of the best performing system TableMiner+, the F-measure for the difficult property matching task is increased by 0:08, for the class and instance matching task by 0:05 and 0:03, respectively.

Bibliographic meta-information and download of the thesis.

 

 

Prof. Dr.-Ing. Margret Keuper

Image Processing

A5, 6, Room B 223

Email: keuper (at) uni-mannheim . de

Phone: +49 621 181 2602

 

I joined the Data and Web Science Group in April 2017 as a Juniorprofessor. My research interests are Computer Vision and Image Processing. More specifically, I am interested in grouping problems such as

 

During my PhD with Thomas Brox at the University of Freiburg, I focused on the segmentation in volumetric bio-medical image data.

Publications

You can also find a full list of my publications on Google Scholar..

2017

Margret Keuper: Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation, in Proc. of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, Oct. 2017, to appear. [pdf]

Anne S. Wannenwetsch, Margret Keuper, and Stefan Roth: ProbFlow: Joint optical flow and uncertainty estimation, in Proc. of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, Oct. 2017, to appear. [pdf]

Yang He, Margret Keuper, Bernt Schiele and Mario Fritz, Learning Dilation Factors for Semantic Segmentation of Street Scenes, 39th German Conference on Pattern Recognition (GCPR), 2017.

Yang He, Wei-Chen Chiu, Margret Keuper, Mario Fritz: STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data Driven Pooling, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [pdf]

Eddy Ilg, Nikolaus Mayer, T. Saikia, Margret Keuper, Alexey Dosovitskiy, Thomas Brox FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [pdf]

2016

Margret Keuper, Thomas Brox: Point-Wise Mutual Information-Based Video Segmentation with High Temporal Consistency. ECCV Workshops (3) 2016: 789-803. [pdf]

Margret Keuper, Thomas Brox: Segmentation in Point Clouds from RGB-D Using Spectral Graph Reduction, Perspectives in Shape Analysis, 155-168

2015

Margret Keuper, Bjoern Andres, Thomas Brox: Motion Trajectory Segmentation via Minimum Cost Multicuts. in Proc. of the IEEE International Conference on Computer Vision (ICCV), 2015: 3271-3279 [pdf]

Margret Keuper, Evgeny Levinkov, Nicolas Bonneel, Guillaume Lavoué, Thomas Brox, Bjoern Andres: Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts. in Proc. of the IEEE International Conference on Computer Vision (ICCV), 2015: 1751-1759 [pdf]

2014

Fabio Galasso, Margret Keuper, Thomas Brox, Bernt Schiele: Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation. in Proc. of the CVPR 2014: 49-56 [pdf]

2013

Thorsten Schmidt, Jasmin Dürr, Margret Keuper, Thomas Blein, Klaus Palme, Olaf Ronneberger: Variational attenuation correction in two-view confocal microscopy. BMC Bioinformatics 14: 366 (2013) [paper]

Margret Keuper, Thorsten Schmidt, Maja Temerinac-Ott, Jan Padeken, Patrick Heun, Olaf Ronneberger, Thomas Brox: Blind Deconvolution of Widefield Fluorescence Microscopic Data by Regularization of the Optical Transfer Function (OTF). CVPR 2013: 2179-2186 [pdf]

Thorsten Schmidt, Jasmin Dürr, Margret Keuper, Thomas Blein, Klaus Palme, Olaf Ronneberger: Variational attenuation correction of two-view confocal microscopic recordings. ISBI 2013: 169-172 [paper]

2012

Thorsten Schmidt, Margret Keuper, Taras Pasternak, Klaus Palme, Olaf Ronneberger: Modeling of Sparsely Sampled Tubular Surfaces Using Coupled Curves. DAGM/OAGM Symposium 2012: 83-92 [pdf]

Margret Keuper, Maja Temerinac-Ott, Jan Padeken, Patrick Heun, Thomas Brox, Hans Burkhardt, Olaf Ronneberger: Blind deconvolution with PSF regularization for wide-field microscopy. ISBI 2012: 1292-1295 [pdf]

2011

Margret Keuper, Thorsten Schmidt, Marta Rodriguez-Franco, Wolfgang Schamel, Thomas Brox, Hans Burkhardt, Olaf Ronneberger: Hierarchical Markov Random Fields for Mast Cell Segmentation in Electron Microscopic Recordings. In Proc. of the International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 2011, pages 973-978. [pdf]

2010

M. Keuper, R. Bensch, K. Voigt, A. Dovzhenko, K. Palme, H. Burkhardt, O. Ronneberger: Semi-Supervised Learning of Edge Filters for Volumetric Image Segmentation , inProc. of the 32nd DAGM Symposium 2010, LNCS, pages 462-471. [pdf]

M. Keuper, T. Schmidt, J. Padeken, P. Heun, K. Palme, H. Burkhardt, O. Ronneberger: 3D Deformable Surfaces with Locally Self-Adjusting Parameters - A Robust Method to Determine Cell Nucleus Shapes, in Proc. of the ICPR, 20th International Conference on Pattern Recognition, 2010, pages 2254-2257. [pdf]

M. Keuper, J. Padeken, P. Heun, H. Burkhardt, O. Ronneberger: Mean Shift Gradient Vector Flow: A Robust External Force Field for 3D Active Surfaces , in Proc. of the ICPR, 20th International Conference on Pattern Recognition, 2010, pages 2784-2787. [pdf]

M. Temerinac, M. Keuper and H. Burkhardt:
Evaluation of a New Point Clouds Registration Method based on Group Averaging Features, in Proc. of the ICPR, 20th International Conference on Pattern Recognition, 2010, pages 2452-2455. [pdf]

2009

M. Keuper, J. Padeken, P. Heun, H. Burkhardt, O. Ronneberger:
A 3D Active Surface Model for the accurate Segmentation of Drosophila Schneider Cell Nuclei and Nucleoli. [pdf] In Proc. of the ISVC 2009, LNCS, pages: 865-874.

Awards and Nominations

  • Winner of the CVPR 2017 Multiple Object Tracking Challenge
  • Outstanding Reviewer Award, BMVC 2017
  • Outstanding Reviewer Award, ECCV 2016
  • Outstanding Reviewer Award, ICCV 2015