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.

I recently moved to the Technical University Munich's TUM School of Computation, Information and Technology in Heilbronn where I will continue my research program. A web presence will be built there in due time. See here for my institutional tab. 

Group Leader

Prof. Dr. Maribel Acosta

Members

Tim Schwabe, M.Sc.

Affiliated

Nadine Kais

Nicole Krug

Chang Qin, M.Sc.

Zenon Zacouris, B.Sc.

    2023

  • Parker: Data Fusion through Consistent Repairs using Edit Rules under Partial Keys
    Bronselaer, A., & Acosta, M.
    Information Fusion
  • Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
    Schwabe, T., & Acosta, M.
    arXiv

    • 2022

  • Uncertainty in coupled models of cyber-physical systems
    Acosta, M., Hahner, S., Koziolek, A., Kühn, T., Mirandola, R., & Reussner, R. H.
    In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, MODELS 2022, Montreal, Quebec, Canada, October 23-28, 2022 (pp. 569–578) ACM
  • Robust Query Processing for Linked Data Fragments
    Heling, L., & Acosta, M.
    Semantic Web
  • Federated SPARQL Query Processing over Heterogeneous Linked Data Fragments
    Heling, L., & Acosta, M.
    In WWW ′22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022 (pp. 1047–1057) ACM
    Federated SPARQL Query Processing over HeterogeneousLinked Data Fragments
  • Utility-aware Semantics for Alternative Service Expressions in Federated SPARQL Queries
    Heling, L., & Acosta, M.
    In 2022 IEEE International Conference on Web Services, ICWS IEEE
    pdf
  • AFRNN: Stable RNN with Top Down Feedback and Antisymmetry
    Schwabe, T., Glasmachers, T., & Acosta, M.
    In Proceedings of the 14th Asian Conference on Machine Learning (ACML). To Appear
    AFRNN: Stable RNN with Top Down Feedback and Antisymmetry

    • 2021

  • Pay-as-you-go Population of an Automotive Signal Knowledge Graph
    Svetashova, Y., Heling, L., Schmid, S., & Acosta, M.
    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., & Acosta, M.
    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

    • 2018

  • Query Processing over Graph-structured Data on the Web
    Acosta, M.
    Doctoral thesis, Karlsruhe Institute of Technology, Germany
    pdf

    2021

  • Decentralized Query Processing over Heterogeneous Sources of Knowledge Graphs
    Heling, L.
    Doctoral thesis, Karlsruhe Institute of Technology, Germany
  • Learning Latent Features using Stochastic Neural Networks on Graph Structured Data
    Weller, T.
    Doctoral thesis, Karlsruhe Institute of Technology, Germany
    Learning Latent Features using Stochastic Neural Networks on Graph Structured Data

Deep Learning for Optimization of Spatial Light Modulators

Spatial Light Modulators can be used to modulate the effective shape of light, e.g., a laser beam. They are thus useful in industrial applications like laser cutting where beam shapes need to be adapted quickly. However, generating complex shapes is time-consuming and error-prone using current algorithms based on Fourier transformations. In this work, a deep learning approach that automatically generates the correct modulations to obtain the desired shape should be explored. The Thesis is conducted in cooperation with the company LIDROTEC(https://www.lidrotec.de/).

DataNinja Spring School

The group recently attended the DataNinja Spring School, held online from the 23rd to 25th of March. The Spring School was organized by the University of Bielefeld and focused on “Artificial Intelligence – perspectives and challenges”. Maribel Acosta held a talk on “Symbolic and Sub-symbolic Representations of Knowledge Graphs”

Tutorial at the KnowGraphs Winter School 2022

Maribel Acosta presented a tutorial on "Querying Federations of Knowledge Graphs" at the KnowGraphs Winter School 2022. The Marie Curie ITN-ETN KnowGraphs is a training network that focus on research about Knowledge Graphs. The Winter School 2022 was held online from 31.01 to 02.02.2022, and comprised 9 tutorials and 3 data challenges.

About the INI

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.

Contact

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

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

sekretariat@ini.rub.de
www.ini.rub.de