bibliography.bib

% Encoding: UTF-8

@misc{ClusterOfExcellence,
    author= {School of Business and Economics - RWTH Aachen University},
    year  = {2021},
    title = {Cluster of Excellence "Internet of Production"},
    note  = {\url{https://www.wiwi.rwth-aachen.de/cms/Wirtschaftswissenschaften/Forschung/Aktuell/Forschungsprojekte/~bddtc/Exzellenzcluster-Internet-of-Production/lidx/1/},
        Last accessed on 2022-03-02},
}

@misc{InternetProduction,
    author= {{RWTH Aachen University}},
    year  = {2022},
    title = {Internet of Production},
    note  = {\url{https://www.rwth-aachen.de/go/id/bktz/lidx/1}, 
             Last accessed on 2022-01-14},
}

@misc{ProductionEngineering,
    author= {{RWTH Aachen University}},
    year  = {2017},
    title = {Digital Connected Production},
    note  = {\url{https://www.rwth-campus.com/wp-content/uploads/2015/01/Broschuere-Cluster-Productionstechnik.pdf}, 
        Last accessed on 2022-01-15},
}

@misc{Industrie40,
    author= {{Federal Ministry for Economic Affairs and Climate Action}},
    year  = {2022},
    title = {What is Industrie 4.0?},
    note  = {\url{https://www.plattform-i40.de/IP/Navigation/EN/Industrie40/WhatIsIndustrie40/what-is-industrie40.html}, 
        Last accessed on 2021-12-12},
}

@incollection{Psarommatis22,
title = {Chapter 9 - The role of big data analytics in the context of modeling design and operation of manufacturing systems},
editor = {Dimitris Mourtzis},
booktitle = {Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology},
publisher = {Elsevier},
pages = {243-275},
year = {2022},
isbn = {978-0-12-823657-4},
doi = {https://doi.org/10.1016/B978-0-12-823657-4.00012-9},
url = {https://www.sciencedirect.com/science/article/pii/B9780128236574000129},
author = {Foivos Psarommatis and Paul Arthur Dreyfus and Dimitris Kiritsis},
keywords = {Big data, Industry 4.0, Mass customization, Semantics, Zero defect manufacturing},
abstract = {Contemporary industries are generating a huge amount of data coming from a high variety of sources, such as machine controllers, corporate systems, sensors, operators, and more. Therefore, the need for the utilization of the information generated, also known as big data, has emerged. Although data importance is well known, very often in manufacturing industries data are abundant and often underexploited leading to poor performance and efficiency. Big data are part of Industry 4.0 concept and also serve as key element for the Industry 4.0 technologies. But still there is a lot of ground to be covered in the exploitation and industrialization of those technologies. Modeling, design and operation of modern manufacturing systems, where Industry 4.0 technologies are applied, are three fundamental processes in the contemporary manufacturing domain that require constant attention and continuous optimization in order for the manufacturer to stay competitive and improve the efficiency and reduce the environmental impact.}
}

@misc{ClusterExcellence,
    author= {{RWTH Aachen University}},
    year  = {2021},
    title = {Clusters of Excellence},
    note  = {\url{https://www.rwth-aachen.de/cms/root/Forschung/Projekte/~ohg/Exzellenzcluster/lidx/1/}, 
             Last accessed on 2022-01-14},
}

@inbook{Keet13-1,
author="Keet, C. Maria",
title="{Open World Assumption}",
bookTitle="Encyclopedia of Systems Biology",
year="2013",
publisher="Springer New York",
address="New York, NY",
pages="1567--1567",
isbn="978-1-4419-9863-7",
doi="10.1007/978-1-4419-9863-7_734",
url="https://doi.org/10.1007/978-1-4419-9863-7_734"
}

@inbook{Keet13-2,
author="Keet, C. Maria",
title="{Closed World Assumption}",
bookTitle="Encyclopedia of Systems Biology",
year="2013",
publisher="Springer New York",
address="New York, NY",
pages="415--415",
isbn="978-1-4419-9863-7",
doi="10.1007/978-1-4419-9863-7_731",
url="https://doi.org/10.1007/978-1-4419-9863-7_731"
}

@techreport{Noy01,
    abstract = {In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of Artificial-Intelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System},
    author = {Noy, Natalya F. and Mcguinness, Deborah L.},
    title = {{Ontology Development 101: A Guide to Creating Your First Ontology}},
    keywords = {engineering ontology},
    institution = {Stanford University},
    address = {Stanford, CA},
    year = {2001},
    url = {https://protege.stanford.edu/publications/ontology_development/ontology101.pdf},
}

@inproceedings{Peng13,
  author={Peng, Li and Man, Yuan},
  booktitle={Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)}, 
  title={{A Novel Data Exchange Model Based on P2P and Ontology}}, 
  year={2013},
  volume={},
  number={},
  pages={1693-1696},
  doi={10.1109/MEC.2013.6885329}}
  
@inproceedings{Zhou21,
  author={Zhou, Xinjie and Gao, Guyue and Ming, Xinguo and Wang, Liya and Yin, Dao and Ma, Xiaohong},
  booktitle = {2021 International Conference on Service Science (ICSS)},
  title = {{Task-oriented Knowledge Representation and Ontology Modeling for Complex Product Design}},
  year = 2021,
  pages={30-37},
  doi={10.1109/ICSS53362.2021.00013}
}

@article{Gruber93,
  title={{A Translation Approach to Portable Ontology Specifications}},
  author={Gruber, Thomas R.},
  journal={Knowledge Acquisition},
  year={1993},
  volume={5},
  pages={199-220},
  url = {http://www-ksl.stanford.edu/kst/what-is-an-ontology.html}
}

@inproceedings{Kalfoglou05,
  author = {Kalfoglou, Yannis and Schorlemmer, Marco},
  biburl = {https://www.bibsonomy.org/bibtex/2de190a0cc9789a718bef2ffea428e309/fparreiras},
  booktitle = {Semantic Interoperability and Integration},
  editor = {Kalfoglou, Y. and Schorlemmer, M. and Sheth, A. and Staab, S. and Uschold, M.},
  keywords = {mapping ontology owl},
  number = 04391,
  optaddress = {Dagstuhl, Germany},
  optannote = {Keywords: ontology mapping},
  url = {http://drops.dagstuhl.de/opus/volltexte/2005/40},
  publisher = {Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany},
  series = {Dagstuhl Seminar Proceedings},
  title = {{Ontology Mapping: The State of the Art}},
  year = 2005
}

@article{Georgieva21,
  title={{Semantic Web Technologies for Big Data Modeling from Analytics Perspective: A Systematic Literature Review}},
  author={Georgieva-Trifonova, Tsvetanka and Galabov, Miroslav},
  journal={Balt. J. Mod. Comput.},
  year={2021},
  volume={9}
}

@inproceedings{Wache01,
    author = {Wache, H. and Vögele, T. and Visser, U. and Stuckenschmidt, H. and Schuster, G. and Neumann, H. and Hübner, S.},
    title = {{Ontology-based Integration of Information - A Survey of Existing Approaches}},
    booktitle={Ois@ ijcai},
    year = {2001},
    pages = {108--117}
}

@misc{RFC3986,
    author= {Berners-Lee, T. et al.},
    year  = {2005},
    title = {{RFC3986 - Uniform Resource Identifier (URI): Generic Syntax}},
    note  = {\url{https://datatracker.ietf.org/doc/html/rfc3986},
        Last accessed on 2022-06-06},
}

@article{Ramis21,
  title={{Comparing Ontologies and Databases: A Critical Review of Lifecycle Engineering Models in Manufacturing}},
  author={Ramis Ferrer, Borja and Mohammed, Wael M. and Ahmad, Mussawar and Iarovyi, Sergii and Zhang, Jiayi and Harrison, Robert and Martinez Lastra, Jose Luis},
  journal={Knowledge and Information Systems},
  year={2021},
  abstract= "The literature on the modeling and management of data generated through the lifecycle of a manufacturing system is split into two main paradigms: product lifecycle management (PLM) and product, process, resource (PPR) modeling. These paradigms are complementary, and the latter could be considered a more neutral version of the former. There are two main technologies associated with these paradigms: ontologies and databases. Database technology is widespread in industry and is well established. Ontologies remain largely a plaything of the academic community which, despite numerous projects and publications, have seen limited implementations in industrial manufacturing applications. The main objective of this paper is to provide a comparison between ontologies and databases, offering both qualitative and quantitative analyses in the context of PLM and PPR. To achieve this, the article presents (1) a literature review within the context of manufacturing systems that use databases and ontologies, identifying their respective strengths and weaknesses, and (2) an implementation in a real industrial scenario that demonstrates how different modeling approaches can be used for the same purpose. This experiment is used to enable discussion and comparative analysis of both modeling strategies.",
  doi = {https://doi.org/10.1007/s10115-021-01558-4},
}

@article{Uschold15,
	doi = {10.3233/ao-150158},
	publisher = {Ios Press},
	number = {3-4},
	author = {Uschold, Michael},
	year = {2015},
	volume = {10},
	pages = {243--258},
	journal = {Applied Ontology},
	title = {{Ontology and Database Schema: What?s the Difference?}}
}

@book{Russell16,
  author = {Russell, Stuart and Norvig, Peter},
  edition = 3,
  publisher = {Prentice Hall},
  title = {{Artificial intelligence (Third edition, Global edition)}},
  year = 2016
}

@misc{Sequeda12,
    author= {Sequeda, Juan},
    year  = {2012},
    title = {{Introduction to: Open World Assumption vs Closed World Assumption}},
    note  = {\url{https://www.dataversity.net/introduction-to-open-world-assumption-vs-closed-world-assumption},
        Last accessed on 2022-06-04},
}

@article{Poveda22,
  title={LOT: An industrial oriented ontology engineering framework},
  author={Poveda-Villal{\'o}n, Mar{\'\i}a and Fern{\'a}ndez-Izquierdo, Alba and Fern{\'a}ndez-L{\'o}pez, Mariano and Garc{\'\i}a-Castro, Ra{\'u}l},
  journal={Engineering Applications of Artificial Intelligence},
  volume={111},
  pages={104755},
  year={2022},
  publisher={Elsevier}
}

@inproceedings{Kharlamov18,  
author={Kharlamov, Evgeny and Martin-Recuerda, Francisco and Perry, Brandon and Cameron, David and Fjellheim, Roar and Waaler, Arild},  
booktitle={2018 IEEE International Conference on Big Data (Big Data)},   
title={{Towards Semantically Enhanced Digital Twins}},   
year={2018},  
volume={},  
number={},  
pages={4189-4193},  
doi={10.1109/BigData.2018.8622503}}


@misc{Maastricht20,
    author= {{Institute of Data Science - Maastricht University}},
    year  = {2020},
    title = {{Using Ontologies}},
    note  = {\url{https://maastrichtu-ids.github.io/best-practices/docs/using-ontologies/},
        Last accessed on 2022-06-13},
}

@inbook{Gangemi09,
author="Gangemi, Aldo and Presutti, Valentina",
title="{Ontology Design Patterns}",
bookTitle="Handbook on Ontologies",
year="2009",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="221--243",
abstract="Computational ontologies in the context of information systems are artifacts that encode a description of some world, for some purpose. Under the assumption that there exist classes of problems that can be solved by applying common solutions (as it has been experienced in software engineering), we envision small, task-oriented ontologies with explicit documentation of design rationales. In this chapter, we describe components called Ontology Design Patterns (OP), and methods that support pattern-based ontology design.",
isbn="978-3-540-92673-3",
doi="10.1007/978-3-540-92673-3_10",
url="https://doi.org/10.1007/978-3-540-92673-3_10"
}

@article{Presutti08,
  title={{NeOn Deliverable D2. 5.1. A Library of Ontology Design Patterns: Reusable Solutions for Collaborative Design of Networked Ontologies}},
  author={Presutti, Valentina and Gangemi, Aldo and David, Stefano and de Cea, G Aguado and Surez-Figueroa, MC and Montiel-Ponsoda, Elena and Poveda, M},
  journal={NeOn Project. http://www. neon-project. org},
  year={2008}
}

@article{Perez08,
  title={{Neon Methodology for Building Ontology Networks: Ontology Specification}},
  author={P{\'e}rez G{\'o}mez, Asunci{\'o}n and Baonza, Mcs De Figueroa and Villaz{\'o}n, Boris},
  journal={Methodology},
  pages={1--18},
  year={2008}
}

@conference {RodriguezDoncel15,
	title = {{Pattern-based Linked Data Publication: The Linked Chess Dataset Case}},
	booktitle = {Proceedings of the 6th International Workshop on Consuming Linked Data co-located with 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, US, October 12th, 2015},
	volume = {1426},
	year = {2015},
	publisher = {CEUR-WS.org},
	organization = {CEUR-WS.org},
	type = {Technical Report},
	abstract = {<p>This paper discusses the relationship between ontology design patterns (ODPs), data models and linked data, proposing a method that simplifies the task of publishing linked data while adhering to good modeling practices that reuse well-studied ODPs. The proposed process simplifies the tasks of the domain experts but preserves the integrity of the design patterns, favoring a well-designed and well documented data model which fosters data reuse. The work is illustrated with a linked dataset of two million chess games, with the key information mapped to other linked datasets and supported by formalized design patterns. This is the first time a chess dataset is presented as linked data, and an insight on its usefulness is given.</p>
},
	url = {http://dase.cs.wright.edu/publications/pattern-based-linked-data-publication-linked-chess-dataset-case},
	author = {Rodr{\'\i}guez-Doncel, V{\'\i}ctor and Krisnadhi, Adila and Hitzler, Pascal and Cheatham, Michelle and Karima, Nazifa and Amini, Reihaneh},
	editor = {Hartig, Olaf and Sequeda, Juan and Hogan, Aidan}
}

@inproceedings{Jaskolka15,
  title={{Towards an Ontology Design Architecture}},
  author={Jaskolka, Jason and MacCaull, Wendy and Khedri, Ridha},
  booktitle={2015 International Conference on Computational Science and Computational Intelligence (CSCI)},
  pages={132--135},
  year={2015},
  organization={IEEE}
}

%%% MISC
@misc{Shimizu19,
      title={{MODL: A Modular Ontology Design Library}}, 
      author={Shimizu, Cogan and Hirt, Quinn and Hitzler, Pascal},
      year={2019},
      eprint={1904.05405},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

@article{Neuenschwander22,
  author    = {Neuenschwander, Stefanie and Romao, Patricia and Holm, Jürgen and Sariyar, Murat},
  title     = {{Developing an Ontology for Documenting Adverse Events While Avoiding Pitfalls}},
  journal   = {Studies in health technology and informatics},
  volume    = { 289},
  pages     = {166-169},
  year      = {2022},
  doi       = {10.3233/SHTI210885},
  abstract  = {Ontologies promise more benefits than terminologies in terms of data annotation 
      and computer-assisted reasoning, by defining a hierarchy of terms and their 
      relations within a domain. Here, we present central insights related to the 
      development of an ontology for documenting events during interoperative 
      neuromonitoring (IOM), for which we used the Basic Formal Ontology (BFO) as an 
      upper-level ontology. This work has the following two goals: to describe the 
      development of the IOM ontology and to guide the practice with respect to 
      documenting of biomedical events, as available ontologies pose difficulties on 
      certain issues. We address the following issues: (i) differentiate between the 
      sets documentation, identification, continuant and explanation, understanding, 
      occurrent as we had problems in applying the available ontology of adverse 
      events, (ii) covering diseases and injuries in a consistent way, and (iii) 
      deciding on which level to define relations.}
}

@techreport{Rudnicki16,
     title = {{Best Practices of Ontology Development}},
     author = {Rudnicki, Ron and Smith, Barry and Malyuta, Tanya and Mandrick, William},
     year = {2016},
     institution = {CUBRC, Inc.},
     month = {10},
}

@inproceedings{BedenCao21,
  author={Beden, Sadeer and Cao, Qiushi and Beckmann, Arnold},
  booktitle={2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)}, 
  title={{Semantic Asset Administration Shells in Industry 4.0: A Survey}}, 
  year={2021},
  volume={},
  number={},
  pages={31-38},
  doi={10.1109/ICPS49255.2021.9468266}}