---
_id: '7144'
abstract:
- lang: eng
  text: 'The increasing complexity of product configurations demands intelligent systems
    that effectively integrate customer requirements, dependencies, and uncertainties.
    This paper introduces a unified framework combining Knowledge Graphs (KGs) and
    Bayesian Networks (BNs) to enhance the efficiency and adaptability of product
    configuration processes. KGs provide a semantic foundation for product information,
    ensuring interoperability and explicit relationship modeling. BNs enhance this
    through probabilistic reasoning, allowing the system to manage uncertainties and
    dynamically generate optimal configurations. The integration of deterministic,
    rule-based reasoning from ontologies with the probabilistic nature of BNs automates
    suggestions, predicts user preferences, and reduces complexity. This framework
    streamlines user interactions through intelligent form pre-filling and contextually
    relevant suggestions, even under uncertainty. By employing an ontology-based representation
    of BNs, the components fit seamlessly into the KG, creating a cohesive and unified
    framework that balances scalability and user-centric design to address modern
    configuration challenges.                '
author:
- first_name: Stefan
  full_name: Berlik, Stefan
  id: '237529'
  last_name: Berlik
  orcid: 0009-0007-3242-4501
  orcid_put_code_url: https://api.orcid.org/v2.0/0009-0007-3242-4501/work/226068666
- first_name: Mohammad
  full_name: Seidpisheh, Mohammad
  id: '258037'
  last_name: Seidpisheh
  orcid: 0000-0002-2976-9206
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-2976-9206/work/226068667
citation:
  alphadin: '<span style="font-variant:small-caps;">Berlik, Stefan</span> ; <span
    style="font-variant:small-caps;">Seidpisheh, Mohammad</span>: A Unified Framework
    for Intelligent Product Configuration Using Knowledge Graphs and Bayesian Networks.
    In: <span style="font-variant:small-caps;">Manns, M.</span> (Hrsg.): <i>New Paradigms
    for Anticipated Uncertainty. Proceedings of the 10th Changeable, Agile, Reconfigurable
    and Virtual Production Conference (CARV 2025) and the 12th World Mass Customization
    and Personalization Conference (MCPC 2025), Siegen, Germany, September 2025</i>,
    <i>Lecture Notes in Mechanical Engineering</i>. Cham : Springer Nature Switzerland,
    2026, S. 131–141'
  ama: 'Berlik S, Seidpisheh M. A Unified Framework for Intelligent Product Configuration
    Using Knowledge Graphs and Bayesian Networks. In: Manns M, ed. <i>New Paradigms
    for Anticipated Uncertainty. Proceedings of the 10th Changeable, Agile, Reconfigurable
    and Virtual Production Conference (CARV 2025) and the 12th World Mass Customization
    and Personalization Conference (MCPC 2025), Siegen, Germany, September 2025</i>.
    Lecture Notes in Mechanical Engineering. Cham: Springer Nature Switzerland; 2026:131-141.
    doi:<a href="https://doi.org/10.1007/978-3-032-16889-4_13">10.1007/978-3-032-16889-4_13</a>'
  apa: 'Berlik, S., &#38; Seidpisheh, M. (2026). A Unified Framework for Intelligent
    Product Configuration Using Knowledge Graphs and Bayesian Networks. In M. Manns
    (Ed.), <i>New Paradigms for Anticipated Uncertainty. Proceedings of the 10th Changeable,
    Agile, Reconfigurable and Virtual Production Conference (CARV 2025) and the 12th
    World Mass Customization and Personalization Conference (MCPC 2025), Siegen, Germany,
    September 2025</i> (pp. 131–141). Cham: Springer Nature Switzerland. <a href="https://doi.org/10.1007/978-3-032-16889-4_13">https://doi.org/10.1007/978-3-032-16889-4_13</a>'
  bibtex: '@inproceedings{Berlik_Seidpisheh_2026, place={Cham}, series={Lecture Notes
    in Mechanical Engineering}, title={A Unified Framework for Intelligent Product
    Configuration Using Knowledge Graphs and Bayesian Networks}, DOI={<a href="https://doi.org/10.1007/978-3-032-16889-4_13">10.1007/978-3-032-16889-4_13</a>},
    booktitle={New Paradigms for Anticipated Uncertainty. Proceedings of the 10th
    Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV 2025)
    and the 12th World Mass Customization and Personalization Conference (MCPC 2025),
    Siegen, Germany, September 2025}, publisher={Springer Nature Switzerland}, author={Berlik,
    Stefan and Seidpisheh, Mohammad}, editor={Manns, MartinEditor}, year={2026}, pages={131–141},
    collection={Lecture Notes in Mechanical Engineering} }'
  chicago: 'Berlik, Stefan, and Mohammad Seidpisheh. “A Unified Framework for Intelligent
    Product Configuration Using Knowledge Graphs and Bayesian Networks.” In <i>New
    Paradigms for Anticipated Uncertainty. Proceedings of the 10th Changeable, Agile,
    Reconfigurable and Virtual Production Conference (CARV 2025) and the 12th World
    Mass Customization and Personalization Conference (MCPC 2025), Siegen, Germany,
    September 2025</i>, edited by Martin Manns, 131–41. Lecture Notes in Mechanical
    Engineering. Cham: Springer Nature Switzerland, 2026. <a href="https://doi.org/10.1007/978-3-032-16889-4_13">https://doi.org/10.1007/978-3-032-16889-4_13</a>.'
  ieee: S. Berlik and M. Seidpisheh, “A Unified Framework for Intelligent Product
    Configuration Using Knowledge Graphs and Bayesian Networks,” in <i>New Paradigms
    for Anticipated Uncertainty. Proceedings of the 10th Changeable, Agile, Reconfigurable
    and Virtual Production Conference (CARV 2025) and the 12th World Mass Customization
    and Personalization Conference (MCPC 2025), Siegen, Germany, September 2025</i>,
    Siegen, 2026, pp. 131–141.
  mla: Berlik, Stefan, and Mohammad Seidpisheh. “A Unified Framework for Intelligent
    Product Configuration Using Knowledge Graphs and Bayesian Networks.” <i>New Paradigms
    for Anticipated Uncertainty. Proceedings of the 10th Changeable, Agile, Reconfigurable
    and Virtual Production Conference (CARV 2025) and the 12th World Mass Customization
    and Personalization Conference (MCPC 2025), Siegen, Germany, September 2025</i>,
    edited by Martin Manns, Springer Nature Switzerland, 2026, pp. 131–41, doi:<a
    href="https://doi.org/10.1007/978-3-032-16889-4_13">10.1007/978-3-032-16889-4_13</a>.
  short: 'S. Berlik, M. Seidpisheh, in: M. Manns (Ed.), New Paradigms for Anticipated
    Uncertainty. Proceedings of the 10th Changeable, Agile, Reconfigurable and Virtual
    Production Conference (CARV 2025) and the 12th World Mass Customization and Personalization
    Conference (MCPC 2025), Siegen, Germany, September 2025, Springer Nature Switzerland,
    Cham, 2026, pp. 131–141.'
conference:
  end_date: 2025-09-12
  location: Siegen
  name: 10th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV
    2025) and the 12th World Mass Customization and Personalization Conference (MCPC
    2025)
  start_date: 2025-09-09
date_created: 2026-09-07T17:05:29Z
date_updated: 2026-09-08T08:45:48Z
doi: 10.1007/978-3-032-16889-4_13
editor:
- first_name: Martin
  full_name: Manns, Martin
  last_name: Manns
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
page: 131-141
place: Cham
publication: New Paradigms for Anticipated Uncertainty. Proceedings of the 10th Changeable,
  Agile, Reconfigurable and Virtual Production Conference (CARV 2025) and the 12th
  World Mass Customization and Personalization Conference (MCPC 2025), Siegen, Germany,
  September 2025
publication_identifier:
  eisbn:
  - 978-3-032-16889-4
  eissn:
  - 2195-4364
  isbn:
  - 978-3-032-16888-7
  issn:
  - 2195-4356
publication_status: published
publisher: Springer Nature Switzerland
series_title: Lecture Notes in Mechanical Engineering
status: public
title: A Unified Framework for Intelligent Product Configuration Using Knowledge Graphs
  and Bayesian Networks
type: conference
user_id: '220548'
year: '2026'
...