Databases and Information Systems

The research group of Databases and Information Systems at RUB devises novel techniques for managing decentralised data, information, and knowledge with a focus on query optimisation, federated query processing, and data quality.

As part of our research, we also study the application of Machine Learning approaches to solve diverse problems in Data and Knowledge Management. For example, we leverage latent features from sub-symbolic representations of knowledge to uncover hidden patterns that can be used to predict missing assertions or properties in knowledge bases.

    2021

  • Pay-as-you-go Population of an Automotive Signal Knowledge Graph
    Svetashova, Y., Lars Heling,, Stefan Schmid,, & Maribel Acosta,
    In The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings (Vol. 12731, pp. 717–735) Springer
  • Predicting Instance Type Assertions in Knowledge Graphs Using Stochastic Neural Networks
    Weller, T., & Maribel Acosta,
    In 30th ACM International Conference on Information and Knowledge Management (CIKM) ACM
  • Charaterizing RDF graphs through graph-based measures - framework and assessment
    Zloch, M., Maribel Acosta,, Daniel Hienert,, Stefan Conrad,, & Stefan Dietze,
    Semantic Web, 12(5), 789–812
  • 2020

  • SMART-KG: Hybrid Shipping for SPARQL Querying on the Web
    Azzam, A., Javier D. Fernández,, Maribel Acosta,, Martin Beno,, & Axel Polleres,
    In WWW ′20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020 (pp. 984–994) ACM / IW3C2
  • An Infrastructure for Spatial Linking of Survey Data
    Bensmann, F., Lars Heling,, Stefan Jünger,, Loren Mucha,, Maribel Acosta,, Jan Goebel,, et al.
    Data Sci. J., 19, 27
  • Estimating Characteristic Sets for RDF Dataset Profiles Based on Sampling
    Heling, L., & Maribel Acosta,
    In A. Harth, Sabrina Kirrane,, Axel-Cyrille Ngonga Ngomo,, Heiko Paulheim,, Anisa Rula,, Anna Lisa Gentile,, et al. (Eds.), The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings (Vol. 12123, pp. 157–175) Springer
  • Cost- and Robustness-Based Query Optimization for Linked Data Fragments
    Heling, L., & Maribel Acosta,
    In The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I (Vol. 12506, pp. 238–257) Springer
  • Mining Latent Features of Knowledge Graphs for Predicting Missing Relations
    Weller, T., Tobias Dillig,, Maribel Acosta,, & York Sure-Vetter,
    In Knowledge Engineering and Knowledge Management - 22nd International Conference, EKAW 2020, Bolzano, Italy, September 16-20, 2020, Proceedings (Vol. 12387, pp. 158–170) Springer
  • 2019

  • Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences
    Heling, L., Felix Bensmann,, Benjamin Zapilko,, Maribel Acosta,, & York Sure-Vetter,
    In Joint Proceedings of the 1st International Workshop on Knowledge Graph Building and 1st International Workshop on Large Scale RDF Analytics co-located with 16th Extended Semantic Web Conference (ESWC 2019), Portorož, Slovenia, June 3, 2019 (Vol. 2489, pp. 1–12) CEUR-WS.org
  • Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version)
    Heling, L., Felix Bensmann,, Benjamin Zapilko,, Maribel Acosta,, & York Sure-Vetter,
    In The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Portorož, Slovenia, June 2-6, 2019, Revised Selected Papers (Vol. 11762, pp. 285–299) Springer

    2021

  • Learning Latent Features using Stochastic Neural Networks on Graph Structured Data
    Weller, T.
    Doctoral thesis, Karlsruhe Institute of Technology, Germany

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210