Riesen, Kaspar

Profilbild
E-Mail-Adresse
Geburtsdatum
Projekt
Organisationseinheiten
Berufsbeschreibung
Nachname
Riesen
Vorname
Kaspar
Name
Riesen, Kaspar

Filter

2015 - 201922020 - 20212
Filter zurücksetzen

Einstellungen

Autor:in: Witschel, Hans Friedrich ×

Suchergebnisse

Gerade angezeigt 1 - 4 von 4
  • 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
    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
    Publikation
    A Graph-Based Recommender for Enhancing the Assortment of Web Shops
    (2015) Riesen, Kaspar; Witschel, Hans Friedrich; Galliè, Emidio [in: Proceedings of Workshop on Data Mining in Marketing DMM'2015]
    In this work, we consider a situation where multiple Providers (competitors) serve a common market, using a common infrastructure of sales channels. More speci cally, we focus on multiple web shops that are run by the same web shop platform provider. Our goal is to recommend new items to complement the assortment of a provider, based on user behaviour in the other shops of the same platform. For this new problem, we propose to capture information on how items sell together in a shared product co-occurrence graph. We then adapt known graph-based recommenders to the problem. Further criteria for ranking recommended items are derived as part of a case study conducted in the context of IT web shops. They are combined with the scores of the graph recommenders in a nal ranking function. We evaluate this function with data from our case study context and based on judgments of one shop owner. Our results show that a good ranking can be achieved, reflecting the needs of the shop owner.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    How to Support Customer Segmentation with Useful Cluster Descriptions
    (2015) Witschel, Hans Friedrich; Riesen, Kaspar; Loo, Simon [in: Proc. of Industrial Conference on Data Mining ICDM'15, 2015]
    Customer or market segmentation is an important Instrument for the optimisation of marketing strategies and product portfolios. Clustering is a popular data mining technique used to support such segmentation { it groups customers into segments that share certain demographic or behavioural characteristics. In this research, we explore several automatic approaches which support an important task that starts after the actual clustering, namely capturing and labeling the "essence" of segments. We conducted an empirical study by implementing several of these approaches, applying them to a data set of customer representations and studying the way our study participants interacted with the resulting cluster representations. Major goal of the present paper is to nd out which approaches exhibit the greatest ease of understanding on the one hand and which of them lead to the most correct interpretation of cluster essence on the other hand. Our results indicate that using a learned decision tree model as a cluster representation provides both good ease of understanding and correctness of drawn conclusions.
    04B - Beitrag Konferenzschrift