skip to main content
10.1145/3487553.3524661acmconferencesArticle/Chapter ViewAbstractPublication PageswwwConference Proceedingsconference-collections
research-article

Examining the ORKG towards Representation of Control Theoretic Knowledge – Preliminary Experiences and Conclusions

Published:16 August 2022Publication History

ABSTRACT

Control theory is an interdisciplinary academic domain which contains sophisticated elements from various sub-domains of both mathematics and engineering. The issue of knowledge transfer thus poses a considerable challenge w.r.t. transfer between researchers focusing on different niches as well as w.r.t. transfer into potential application domains. The paper investigates the Open Research Knowledge Graph (ORKG) as medium to facilitate such knowledge transfer. In particular it investigates the current state of control theoretic knowledge represented in the ORKG and describes the process of extending that knowledge as well as the observed challenges thereby. The main results are a) a list of best practice suggestions for the ORKG contributions and b) a list of improvement suggestions for the further development of the ORKG and similar platforms. All relevant claims w.r.t. the ORKG are backed by SPARQL queries and some further evaluation code, both publicly available for the sake of reproducibility.

References

  1. Sören Auer. 2018. Towards an Open Research Knowledge Graph. https://doi.org/10.5281/zenodo.1157185Google ScholarGoogle Scholar
  2. Sören Auer, Allard Oelen, Muhammad Haris, Markus Stocker, Jennifer D’Souza, Kheir Eddine Farfar, Lars Vogt, Manuel Prinz, Vitalis Wiens, and Mohamad Yaser Jaradeh. 2020. Improving Access to Scientific Literature with Knowledge Graphs. Bibliothek Forschung und Praxis 44, 3 (2020), 516–529. https://doi.org/doi:10.1515/bfp-2020-2042Google ScholarGoogle ScholarCross RefCross Ref
  3. Dennis S. Bernstein. 2009. Matrix Mathematics: Theory, Facts, and Formulas with Application to Linear Systems Theory (2.ed.). Princeton University Press, Princeton, NJ.Google ScholarGoogle ScholarCross RefCross Ref
  4. Knut Graichen, Michael Treuer, and Michael Zeitz. 2007. Swing-up of the double pendulum on a cart by feedforward and feedback control with experimental validation. Automatica 43, 1 (2007), 63–71.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Mohamad Yaser Jaradeh, Allard Oelen, Kheir Eddine Farfar, Manuel Prinz, Jennifer D’Souza, Gábor Kismihók, Markus Stocker, and Sören Auer. 2019. Open research knowledge graph: next generation infrastructure for semantic scholarly knowledge. In Proceedings of the 10th International Conference on Knowledge Capture. 243–246.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Carsten Knoll. 2022. Ontology of Control Systems Engineering (OCSE) –- Source Repository on GitHub. https://github.com/ackrep-org/ocseGoogle ScholarGoogle Scholar
  7. Carsten Knoll. 2022. ORKG Interaction –- Source Repository on GitHub. https://github.com/ackrep-org/orkg-interactionGoogle ScholarGoogle Scholar
  8. Carsten Knoll and Robert Heedt. 2020. Automatic Control Knowledge Repository –- A Computational Approach for Simpler and More Robust Reproducibility of Results in Control Theory. In Proc. of the 24th International Conference on System Theory, Control and Computing. IEEE, Sinaia.Google ScholarGoogle ScholarCross RefCross Ref
  9. Carsten Knoll and R. Heedt. 2021. Methodnet: Formal Semantic Representations of Methods in Automatic Control. (2021). https://doi.org/10.13140/RG.2.2.23033.54880 submitted to the Journal of Open Research Software (JORS).Google ScholarGoogle Scholar
  10. Carsten Knoll and Robert Heedt. 2021. Tool-based Support for the FAIR Principles for Control Theoretic Results: The’Automatic Control Knowledge Repository’. SYSTEM THEORY, CONTROL AND COMPUTING JOURNAL 1, 1 (2021), 56–67.Google ScholarGoogle Scholar
  11. Carsten Knoll and Klaus Röbenack. 2012. Generation of stable limit cycles with prescribed frequency and amplitude via polynomial feedback. In Proc. of the 9th International Multi-Conference on Systems, Signals and Devices.Google ScholarGoogle ScholarCross RefCross Ref
  12. Jean Lévine. 2011. On necessary and sufficient conditions for differential flatness. Applicable Algebra in Engineering, Communication and Computing 22, 1(2011), 47–90. https://doi.org/10.1007/s00200-010-0137-xGoogle ScholarGoogle ScholarCross RefCross Ref
  13. Mohamed W. Mehrez, Karl Worthmann, George K.I. Mann, Raymond G. Gosine, and Timm Faulwasser. 2017. Predictive Path Following of Mobile Robots without Terminal Stabilizing Constraints. IFAC-PapersOnLine 50, 1 (2017), 9852–9857. https://doi.org/10.1016/j.ifacol.2017.08.907 20th IFAC World Congress.Google ScholarGoogle ScholarCross RefCross Ref
  14. Max Pritzkoleit, Carsten Knoll, and Klaus Röbenack. 2020. Reinforcement Learning and Trajectory Planning based on Model Approximation with Neural Networks applied to Transition Problems. In Proc. of the 21st IFAC World Congress. IFAC, Berlin.Google ScholarGoogle ScholarCross RefCross Ref
  15. Luciana B Sollaci and Mauricio G Pereira. 2004. The introduction, methods, results, and discussion (IMRAD) structure: a fifty-year survey. Journal of the medical library association 92, 3 (2004), 364.Google ScholarGoogle Scholar
  16. Wikidata contributors. 2022. Q6501221 (control theory). https://www.wikidata.org/w/index.php?title=Q6501221&oldid=1570849991 [Online; accessed 31-January-2022].Google ScholarGoogle Scholar

Index Terms

  1. Examining the ORKG towards Representation of Control Theoretic Knowledge – Preliminary Experiences and Conclusions

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        WWW '22: Companion Proceedings of the Web Conference 2022
        April 2022
        1338 pages
        ISBN:9781450391306
        DOI:10.1145/3487553

        Copyright © 2022 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 16 August 2022

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate1,899of8,196submissions,23%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format