Riesen, Kaspar

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Riesen
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Kaspar
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Riesen, Kaspar

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  • Vorschaubild nicht verfügbar
    Publikation
    Natural language-based user guidance for knowledge graph exploration: a user study
    (SciTePress, 2021) Witschel, Hans Friedrich; Riesen, Kaspar; Grether, Loris; Cucchiara, Rita; Fred, Ana; Filipe, Joaquim [in: Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management]
    Large knowledge graphs hold the promise of helping knowledge workers in their tasks by answering simple and complex questions in specialised domains. However, searching and exploring knowledge graphs in current practice still requires knowledge of certain query languages such as SPARQL or Cypher, which many untrained end users do not possess. Approaches for more user-friendly exploration have been proposed and range from natural language querying over visual cues up to query-by-example mechanisms, often enhanced with recommendation mechanisms offering guidance. We observe, however, a lack of user studies indicating which of these approaches lead to a better user experience and optimal exploration outcomes. In this work, we make a step towards closing this gap by conducting a qualitative user study with a system that relies on formulating queries in natural language and providing answers in the form of subgraph visualisations. Our system is able to offer guidance via query recommendations based on a current context. The user study evaluates the impact of this guidance in terms of both efficiency and effectiveness (recall) of user sessions. We find that both aspects are improved, especially since query recommendations provide inspiration, leading to a larger number of insights discovered in roughly the same time.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Filters for graph-based keyword spotting in historical handwritten documents
    (Elsevier, 2020) Stauffer, Michael; Fischer, Andreas; Riesen, Kaspar [in: Pattern Recognition Letters]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Approximate graph edit distance in quadratic time
    (IEEE, 2020) Riesen, Kaspar; Ferrer, Miquel; Bunke, Horst [in: IEEE/ACM Transactions on Computational Biology and Bioinformatics]
    Graph edit distance is one of the most flexible and general graph matching models available. The major drawback of graph edit distance, however, is its computational complexity that restricts its applicability to graphs of rather small size. Recently, the authors of the present paper introduced a general approximation framework for the graph edit distance problem. The basic idea of this specific algorithm is to first compute an optimal assignment of independent local graph structures (including substitutions, deletions, and insertions of nodes and edges). This optimal assignment is complete and consistent with respect to the involved nodes of both graphs and can thus be used to instantly derive an admissible (yet suboptimal) solution for the original graph edit distance problem in Ο(n³) time. For large scale graphs or graph sets, however, the cubic time complexity may still be too high. Therefore, we propose to use suboptimal algorithms with quadratic rather than cubic time for solving the basic assignment problem. In particular, the present paper introduces five different greedy assignment algorithms in the context of graph edit distance approximation. In an experimental evaluation, we show that these methods have great potential for further speeding up the computation of graph edit distance while the approximated distances remain sufficiently accurate for graph based pattern classification.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Matching of matching-graphs - a novel approach for graph classification
    (IEEE, 2020) Fuchs, Mathias; Riesen, Kaspar [in: 2020 25th International Conference on Pattern Recognition (ICPR)]
    Due to fast developments in data acquisition, we observe rapidly increasing amounts of data available in diverse areas. Simultaneously, we observe that in many applications the underlying data is inherently complex, making graphs a very useful and adequate data structure for formal representation. A large amount of graph based methods for pattern recognition have been proposed. Many of these methods actually rely on graph matching. In the present paper a novel encoding of graph matching information is proposed. The idea of this encoding is to formalize the stable cores of specific classes by means of graphs. In an empirical evaluation we show that it can be highly beneficial to focus on these stable parts of graphs during graph classification.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    KvGR: A graph-based interface for explorative sequential question answering on heterogeneous information sources
    (Springer, 2020) Witschel, Hans Friedrich; Riesen, Kaspar; Grether, Loris; Jose, Joemon M.; Yilmaz, Emine; Magalhães, João; Castells, Pablo; Ferro, Nicola; Silva, Mário J.; Martins, Flávio [in: Advances in Information Retrieval. 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020. Proceedings]
    Exploring a knowledge base is often an iterative process: initially vague information needs are refined by interaction. We propose a novel approach for such interaction that supports sequential question answering (SQA) on knowledge graphs. As opposed to previous work, we focus on exploratory settings, which we support with a visual representation of graph structures, helping users to better understand relationships. In addition, our approach keeps track of context – an important challenge in SQA – by allowing users to make their focus explicit via subgraph selection. Our results show that the interaction principle is either understood immediately or picked up very quickly – and that the possibility of exploring the information space iteratively is appreciated.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    On the impact of using utilities rather than costs for graph matching
    (Springer, 09.11.2019) Riesen, Kaspar; Bunke, Horst; Fischer, Andreas [in: Neural Processing Letters]
    The concept of graph edit distance constitutes one of the most flexible graph matching paradigms available. The major drawback of graph edit distance, viz. the exponential time complexity, has been recently overcome by means of a reformulation of the edit distance problem to a linear sum assignment problem. However, the substantial speed up of the matching is also accompanied by an approximation error on the distances. Major contribution of this paper is the introduction of a transformation process in order to convert the underlying cost model into a utility model. The benefit of this transformation is that it enables the integration of additional information in the assignment process.We empirically confirm the positive effects of this transformation on five benchmark graph sets with respect to the accuracy and run time of a distance based classifier.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Graph-based keyword spotting in historical manuscripts using Hausdorff edit distance
    (Elsevier, 2019) Ameri, Mohammad Reza; Stauffer, Michael; Riesen, Kaspar; Bui, Tien Dai; Fischer, Andreas; Fischer, Andreas [in: Pattern Recognition Letters]
    Keyword spotting enables content-based retrieval of scanned historical manuscripts using search terms, which, in turn, facilitates the indexation in digital libraries. Recent approaches include graph-based representations that capture the complex structure of handwriting. However, the high representational power of graphs comes at the cost of high computational complexity for graph matching. In this article, we investigate the potential of Hausdorff edit distance (HED) for keyword spotting. It is an efficient quadratic-time approximation of the graph edit distance. In a comprehensive experimental evaluation with four types of handwriting graphs and four benchmark datasets (George Washington, Parzival, Botany, and Alvermann Konzilsprotokolle), we demonstrate a strong performance of the proposed HED-based method when compared with the state of the art, both, in terms of precision and speed.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Online signature verification based on string edit distance
    (Springer, 2019) Riesen, Kaspar; Schmidt, Roman [in: International Journal on Document Analysis and Recognition]
    Handwritten signatures are widely used and well-accepted biometrics for personal authentication. The accuracy of signature verification systems has significantly improved in the last decade, making it possible to rely on machines in particular cases or to support human experts. Yet, based on only few genuine references, signature verification is still a challenging task. The present paper provides a comprehensive comparison of two prominent string matching algorithms that can be readily used for signature verification. Moreover, it evaluates a recent cost model for string matching which turns out to be particularly well suited for the task of signature verification. On three benchmarking data sets, we show that this model outperforms the two standard models for string matching with statistical significance.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Combining graph edit distance and triplet networks for offline signature verification
    (Elsevier, 2019) Maergner, Paul; Pondenkandath, Vinaychandran; Alberti, Michele; Liwicki, Marcus; Riesen, Kaspar; Ingold, Rolf; Fischer, Andreas [in: Pattern Recognition Letters]
    Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures. A combination of complementary writer models can make it more difficult for an attacker to deceive the verification system. In this work, we propose to combine a recent structural approach based on graph edit distance with a statistical approach based on deep triplet networks. The combination of the structural and statistical models achieve significant improvements in performance on four publicly available benchmark datasets, highlighting their complementary perspectives.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Cross-evaluation of graph-based keyword spotting in handwritten historical documents
    (Springer, 2019) Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Riesen, Kaspar; Conte, Donatello; Ramel, Jean-Yves; Foggia, Pasquale [in: Graph-Based Representations in Pattern Recognition. 12th IAPR-TC-15 International Workshop, GbRPR 2019, Tours, France, June 19-21, 2019. Proceedings]
    In contrast to statistical representations, graphs offer some inherent advantages when it comes to handwriting representation. That is, graphs are able to adapt their size and structure to the individual handwriting and represent binary relationships that might exist within the handwriting. We observe an increasing number of graph-based keyword spotting frameworks in the last years. In general, keyword spotting allows to retrieve instances of an arbitrary query in documents. It is common practice to optimise keyword spotting frameworks for each document individually, and thus, the overall generalisability remains somehow questionable. In this paper, we focus on this question by conducting a cross-evaluation experiment on four handwritten historical documents. We observe a direct relationship between parameter settings and the actual handwriting. We also propose different ensemble strategies that allow to keep up with individually optimised systems without a priori knowledge of a certain manuscript. Such a system can potentially be applied to new documents without prior optimisation.
    04B - Beitrag Konferenzschrift