BatCAT deliverable 4.4: Semantic artefacts, schema alignments and mappings

101137725/BatCAT/WP4/D4.4 D4.4: Semantic artefacts, schema alignments and mappings Grant agreement number: 101137725 Project acronym: BatCAT Project title: Battery Cell Assembly Twin Project website: http://batcat.info/ Project start date: 01.01.2024 Project duration: 42 months Call topic: HORIZON-CL5-2023-D2-01-03 Deliverable type1: Other (O) Related work package: WP4 Due date: 31.12.2025 Actual submission date: 24.12.2025 Responsible beneficiary: UKRI Dissemination level2: Public (PU) Version: V1.0 delivered Abstract: This document provides pointers for the results of BatCAT tasks T4.4 and T4.5. The actual D4.4 content (ontologies and mappings) is located in a public GitHub repository. Also, an overview document is provided as an attachment to this file. The major content of these are: the LBCOLightweight BatCAT Core Ontologies, their mappings to other ontologies (particularly the EMMO) and to answer set programming. 1 Deliverable type: R = Report, P = Prototype, D = Demonstrator, O = Other. 2 Dissemination level: PU = Public, SEN = Sensitive. Version v1 Page 2 of 4 Author list Beneficiary Name Contact e-mail UKRI Silvia Chiacchiera [email protected] AAU Martin Gebser martin.gebs[email protected] GCL Gerard Goldbeck [email protected] IFPEN Martin Petit martin.p[email protected] GCL Daniele Toti [email protected] NMBU Fadi Al Machot fadi.[email protected] DTU Ivano E. Castelli [email protected] NIC Sara Drvarič Talian [email protected] AAU Palina Dubatouka [email protected] AAU Mohamed El Bahnasawi moham[email protected] CPI Liyuan Liu [email protected] IFPEN Carlos Nieto Draghi carlos.n[email protected] OVGU (RPTU) Simon Stephan simon.[email protected] UKRI Ilian T. Todorov ilian.todo[email protected] UKRI Noel Vizcaino noel.viz[email protected].uk OVGU (RPTU) Xueqi Zhang xueqi.z[email protected] Reviewer List Beneficiary Name Contact e-mail NMBU Martin Thomas Horsch martin.tho[email protected] Document history Version Date Reason/comment Revised by 1.0 17.12.2025 Deliverable prepared Silvia Chiacchiera (See author list for all who contributed) 1.0 delivered 24.12.2025 Finalized review/preparation See reviewer list Disclaimer Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the EU nor the CINEA can be held responsible for them. Version v1 Page 3 of 4 Abbreviations and acronyms ASP Answer Set Programming CQ Competency Question EMMO Elementary Multiperspective Material Ontology GPO General Process Ontology IRI Internationalized Resource Identifier LBCO Lightweight BatCAT Core Ontologies LiB Lithium-ion battery OWL Web Ontology Language RFB Redox-flow battery TTL Terse Triple Language (Turtle) BatCAT has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101137725. Contents 1. Executive summary ...................................................................................................................................... 4 2. References .................................................................................................................................................... 5 3. Annex ............................................................................................................................................................ 5 Version v1 Page 4 of 4 1. Executive summary BatCAT D4.4, to which this document is associated, concludes the activities of project tasks T4.4 - Metadata standardization and T4.5 - Schema alignment and mapping. This document serves only as a container/pointer to the main results that constitute D4.4: the LBCO repository located at [1] and the .pdf document ``BatCAT ontologies and mappings: an overview”, attached to the present [A1]. See references therein for pointers to the external ontologies and assets they are connected to. The LBCO set of ontologies has been developed to cover use cases on vanadium redox-flow batteries (RFB) and lithium-ion batteries (LIB). Users of BatCAT tools will be practitioners and operators in manufacturing, modelling and characterization, as well as business managers. Accordingly, LBCO contains concepts that will be used (e.g., for data ingest and decision support) by different actors and tools. They are structured in modules and use light logical expressivity. The mappings contain two main parts: mapping/alignment to other (non-LBCO) ontologies, and mapping from OWL to ASP. In the first, the main result is the alignment to a set of EMMO-based ontology modules. For the second, the main outcome is a semi-automatic methodology to transform OWL statements in ASP ones. Then, such statements can be used within a logic-based solver. The activities that led to D4.4 results built strongly on previous work from WP4, namely D4.1 - Data landscape and infrastructure related requirements, especially on the requirements formulated there as competency questions (CQ). Also, strong interactions with other work packages were maintained. This deliverable, together with the D4.3 Demo [2], concludes the activities of BatCAT WP4. The next project phase will focus on the development of digital twins and decision support systems, which will use LBCO at their core, to support internal and external semantic interoperability of data and components. While the bulk of the LBCO ontologies has concluded, they will be updated at [1] taking into account users’ feedback and evolving requirements from the BatCAT project. With use and as the project development advances and more components / tools need to use them, we expect new requirements will arise. 2. References [1]. Lightweight BatCAT Core Ontology (LBCO) - GitHub repository. https://github.com/HE-BatCAT/lbco, 2025. [2] T. Fitschen et al, BatCAT D4.3 - Federated knowledge base demo, video, to appear on Zenodo in December 2025. 3. Annex [A1]. S. Chiacchiera et al, BatCAT ontologies and mappings: An overview, technical report, December 2025. BatCAT ontologies and mappings: an overview Silvia Chiacchiera1, Martin Gebser2, Gerhard Goldbeck3, Martin Petit4, Daniele Toti3, Fadi Al Machot5, Maria Bashir5, Ivano E. Castelli6, Sara Drvariˇc Talian7, Palina Dubatouka2, Mohamed El Bahnasawi2, Liyuan Liu8, Carlos Nieto Draghi9, Simon Stephan10, Ilian T. Todorov1, Noel Vizcaino1, Xueqi Zhang10, and Martin T. Horsch1, 5 1Scientific Computing, UKRI Science and Technology Facilities Council, Daresbury Laboratory, Sci-Tech Daresbury, Warrington WA4 4AD, United Kingdom 2Department of Artificial Intelligence and Cybersecurity, University of Klagenfurt, Klagenfurt, Austria 3Goldbeck Consulting Ltd, St John’s Innovation Centre, Cambridge CB4 0WS, Cambridge, United Kingdom 4IFP Energies Nouvelles, 69360 Solaize, France 5Material Theory and Informatics, Norwegian University of Life Sciences, P.O. Box 5003, 1432 ˚As, Norway 6Department of Energy Conversion and Storage, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark 7Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, Ljubljana 1000, Slovenia 8Centre for Process Innovation, Darlington DL1 1GL, United Kingdom 9IFP Energies Nouvelles, 1–4 Av. du Bois Pr´eau, 92852 Rueil-Malmaison, France 10Department of Mechanical and Process Engineering, RPTU Kaiserslautern, P.O. Box 3049, 67653 Kaiserslautern, Germany December 2025 Abstract Within the Horizon Europe project BatCAT (Battery Cell Assembly Twin), a set of ontologies has been developed to cover use cases on vanadium redox-flow batteries and lithium-ion batteries. Users of our tools 1 will be practitioners and operators in manufacturing, modelling and characterization, as well as business managers. The ontologies in OWL will be the basis for conceptualization at lower and higher logical expressivity (as the YAML data model used within the LinkAhead database and the answer set programming axioms for logic-based optimization). The ontologies will support data ingest and retrieval and semantic interoperability within BatCAT tools and with external components. Here we provide an overview of these ontologies, and of their alignment or mapping to related ones (including BattINFO and the EMMO). Finally we present work on the translation of the ontologies (axioms and facts) from OWL to answer set programming. 1 Introduction BatCAT1considers three major and interconnected aspects of batteries’ life cycle: Battery design, battery manufacturing, and battery operation (“in-use”). The central focus of the project is providing a digital twin of battery manufacturing, integrating (physics-based and data-driven) modelling, characterization and sensor data. Two very different types of batteries are considered, vanadium redox-flow batteries (RFBs) and lithium-ion batteries (LiBs). The main user-facing components will be a knowledge base for batteries, digital twins of both the manufacturing line and batteries in-operando, and an interpretable industrial decision support system (IIDSS). These in turn rely on physics-based and data-driven models, validated and parameterized by characterization data. Within the IIDSS module, two major types of optimization will be used: Multi-criteria optimization (MCO) and answer set programming (ASP). The first one is suited for numerical optimization, whereas the second targets problems formulated in logical terms, with discrete variables and in a finite domain (e.g., task scheduling, resource allocation, procedural compliance). A requirements analysis was performed [13] leading to a collection of user stories and a first version of competency questions [6].2To cover all needed concepts, we identified a set of ontology modules, that have been developed: (a) Higher level; (b) Optimization, decision support, design; (c) Twinning; (d) Data and metadata; (e) Modelling and simulation; (f) Characterization; (g) Manufacturing; (h) Battery (LiB and RFB). In parallel, we have evaluated coverage by relevant pre-existing ontologies and standards: these have been used to design mappings and alignments. In this document we first briefly present the LBCO-Lightweight BatCAT Core Ontologies [7], then their alignment with top-level ontologies, in particular to the EMMO-Elementary Multiperspective Material Ontology. Next we describe an example mapping to GPO-General Process Ontology. Finally we 1https://www.batcat.info/ 2In [6] we collected 56 user stories grouped under 23 epics and about 50 competency questions, following the LOT methodology [17]. Also, the relations between characterization methods, characterization devices and measured quantities was given there in tabular form. 2 Figure 1: Left panel: list of modules and color code used in following figures. Right panel: visual notation adapted from Chowlk (extract) [5]. summarize work to translate OWL axioms and facts into ASP ones, which will serve as a base for the logic-based optimization. During the course of the project, and as ontology implementation proceeded, the initial competency questions have been updated: the current ones and their mapping to LBCO ontology entities is given in App. A. 2 LBCO modules The set of modules under development is called LBCO – Lightweight BatCAT Core Ontologies.They are written in OWL (in Turtle format), starting by design from lower logical expressivity, then increasing complexity iteratively and only if motivated by application needs. The development is done in a public GitHub repository [7]. All modules (b) - (h) are aligned with (a), however some are vertical [(e) - (g)] and other transversal [(b) - (d) and (h)]: this means that, for example, the data module (e.g., describing uncertainties and data quality protocols) needs to address corresponding issues from modelling, characterization and manufacturing. In Figure 1, we list the modules and give the graphical notation used in the repository. In the rest of this section we give the scope of each module. Beside the individual modules, for convenience, the repository contains also a file with all LBCO modules3. Higher level concepts (higher.ttl) A module to facilitate a structuring of and connection between the lower modules, and a possible alignment of LBCO with top level ontologies. In Figure 2 we show its class structure. 3https://github.com/HE-BatCAT/lbco/blob/main/all_LBCO_modules_combined/all_ lbco_modules_combined.ttl 3 Figure 2: Classes in the higher.ttl module, version dating December 2025. This and similar figures were generated using the Prot´eg´e tool [15], see also https://protege.stanford.edu/. Modelling and simulation (modelling.ttl) A module about computational modelling and related processes, as model parametrization and validation, and the involved data. Characterization (characterization.ttl) Focuses on off-line characterization. On-line characterization (i.e., that is integrated with the manufacturing process itself, e.g., via sensors) is addressed in the manufacturing module. Manufacturing (manufacturing.ttl) A module about manufacturing of products, via intermediate ones. Includes on-line characterization (i.e., sensors), actuators, product and process KPIs. Optimization, decision support, design (optimization.ttl) A module about optimization problems, either numerical or logic-based. It allows to state a problem and its solution. Twinning (twinning.ttl) A module about twinning, a particular type of computational modelling. In fact, digital twinning in particular involves models (typically data-driven, or hybrid, but not necessarily) that are synchronized with the physical world making use of sens or or characterization data. It allows to describe the twin functionalities. 4 Data and metadata (data.ttl) A module about (meta)data aspects, such as uncertainties, data quality protocols, and metadata schemas used to annotate data. Battery (LiB and RFB) (battery.ttl) A module containing all the battery specific information, such as battery manufacturing processes and battery product parts. Two major types of batteries are considered: Lithium-ion batteries and redox-flow batteries. This module is by far the largest of LBCO, as it expands on many of the different dimensions tackled by the others. 2.1 LBCO evaluation The LBCO ontologies have been evaluated using OOPS! [18] and FOOPS! [11] tools: the first one detected no critical warning, and the second one gave a score larger than 60 % using the “File” modality. Use of these tools on previous versions has helped us improve the ontology quality and FAIRness. We note that the current FOOPS! tool is a development (beta) version, so the numerical score is to be viewed in that light.4 2.2 LBCO Accessibility and persistence The LBCO ontologies, alongside their mappings discussed in this document, are available in a public GitHub repository [7]. The persistent URL https: //purl.org/lbco and the URI https://batcat.info/semantics/core both redirect to [7]. 3 Related work There is a large amount of literature on digital twins in industry and on battery digitalization: we cite here only the most relevant for our work. Specific to batteries, we highlight the Battery interface ontology (BattINFO) [8] (a collection of EMMO domain modules, cf. Sec. 4.1), the Battery Value Chain Ontology (BVCO) [8], that describes manufacturing steps and their inputs and outputs. For digital twins, the standard ISO 30173:2023 [14] provides a reference vocabulary, and Barros et al. [2] analyzes requirements and suggests an ontology addressing them. To describe sensors and actuators, the W3C Semantic Sensor Network (SSN) [19] is available. For characterization data, the CHAMEO ontology [16], is an ontologization of the CHADA CEN Workshop Agreement [4]. 4E.g., we noted an issue that we believe arises when domain and range are used in parallel with restrictions on some property, causing some misreading of the ontology and an artificial score reduction. This is to be further tested and explored, first on our side, and then in connection with the FOOPS! tool developers if needed. 5 ID Competency Question (CQ) / Natural language sentence (fact) (FA) / Example Query (EQ) Answer [for CQ only] IRI of LBCO most relevant classes/properties CQ CRO 1 What are the main properties of the electrode? surface chemical composition; surface area; conductivity; surface hydrophilicity; exchange current rate; tortuosity; porosity; surface stability (towards modifications) lbco:electrode; lbco:has property CQ CRO 2 What are the main properties of the electrolyte? Composition (salts; solvents); purity; transport properties (viscosity; ionic conductivity); mass density lbco:electrolyte; lbco:has property CQ CRO 3 What are the major time scales for process industry? (to classify various types of DTs) Industrial safety; intermediate; planning lbco:time scale CQ CRO 4 What are the main components of a RFB? Bipolar plates; Membranes; Carbon felt; Electrolyte lbco:redox flow battery; lbco:has part CQ CRO 4.1 What are the main components of a LiB/NiB? Active material; Binder; Percolant; Slurry solvent; Electrode; Separator; Electrolyte; Collector; Battery cell; Casing; Coating layer; Full cell lbco:lithium ion battery; lbco:has part CQ CRO 8 What are the high-level types of optimization problems that can benefit from logic-based approaches? Scheduling (e.g. of maintenance); Allocation of resources (e.g. machines/humans for tasks); Compatibility checks (e.g. between materials); Compliance (e.g. of a process with regulation); Process design (e.g. simulation / experiment); Process sequence validation (e.g. that the ordering of subprocesses does not break dependencies) lbco:logic based problem Continued on next page... 12 Table 2 – ... continued from previous page ID Competency Question (CQ) / Natural language sentence (fact) (FA) / Example Query (EQ) Answer [for CQ only] IRI of LBCO most relevant classes/properties CQ CRO 9 What are the main scales for processes in RFB? Molecular scale; Fiber scale; Pore scale; Cell scale; Stack scale; Battery system scale; Energy system scale lbco:rfb length scale FA CRO 3 Materials incompatibilities need to be pointed out (e.g. mixtures that are unsafe) n/a - fact lbco:is incompatible with; lbco:is material incompatible with material FA CRO 4 A sample or a product belong to a batch n/a - fact lbco:batch CQ DAT 3.2 What method is used to process the raw data (and obtain processed data)? Conversion; extraction; incomplete dataset removal; incomplete dataset padding lbco:has data processing method; lbco:has data processing info; lbco:data processing method CQ DAT 1 Is the data(set) complete with respect to a given data structure? Yes; No lbco:is complete dataset CQ DAT 2 Does the data(set) contain dummy values? Yes; No lbco:contains dummy values CQ DAT 4 What types of data quality evaluation methods (checks/guarantees) are available within BatCAT? Human assessment; Quality insurance procedures (e.g. regular calibration checks); Automated repeatability tests lbco:data quality evaluation method CQ DAT 5 What is the average value and error of property X over a certain set? n/a - case-specific lbco:data; lbco:has average; lbco:has error CQ DAT 7 What methods are available in BatCAT to quantify uncertainties in general? Error propagation; Error estimate via statistical methods lbco:uncertainty quantification method Continued on next page... 13 Table 2 – ... continued from previous page ID Competency Question (CQ) / Natural language sentence (fact) (FA) / Example Query (EQ) Answer [for CQ only] IRI of LBCO most relevant classes/properties CQ DAT 8 What metadata schemas are used to annotate datasets to ensure FAIR compliance? n/a - case specific lbco:is annotated using scheme CQ EXP 3.3 What are the high-level types of characterization needed for RFB or LiB? Electrochemical characterization; Physicochemical characterization; Rheological characterization lbco:electrochemical characterization technique; lbco:physicochemical characterization technique; lbco:rheological characterization technique FA EXP 1 A characterization technique involves a sample + some instrument (characterization device) and evaluates (as output) some physical quantities n/a - fact lbco:characterization technique CQ MAN 3 What are the main properties of a sensor? type/measured variable; connection type (WIFI/Bluethooth; Optical fiber; wired electrical cable) lbco:sensor; lbco:observes; lbco:property; lbco:has connection; lbco:connection CQ MAN 3.2 What are the sensor types for online characterization of LIB? Accelerometer; Temperature sensor (Thermocouple); vibration sensor; Gas-detection sensor (CO / CO2 sensor); Power sensor (Power monitoring clip); pressure sensor lbco:sensor CQ MAN 4 What are the main (manufacturing) quantities to monitor (process KPIs)? Energy consumption; Equipment performance; Waste produced (amount); Product quality lbco:manufacturing process; lbco:has process kpi CQ MAN 5.1 What are the main ordered steps in the LIB/NIB manufacturing process? Mixing; Coating; Drying; Calendering; Cutting; Assemblying; Filling; Forming; Degassing lbco:lib manufacturing process step Continued on next page... 14 Table 2 – ... continued from previous page ID Competency Question (CQ) / Natural language sentence (fact) (FA) / Example Query (EQ) Answer [for CQ only] IRI of LBCO most relevant classes/properties CQ MAN 5.2 What are the main ordered steps in the RFB manufacturing process? Stack assembly; Stack compression; Stack sealing (glueing); Tanks assemblying lbco:rfb manufacturing process step CQ MAN 6.1 What are the LIB/NIB battery (product) design parameters? Active material (choice); Electrolyte Formulation; Particle size distribution; Electrolyte composition; Separator; Cell size lbco:has product design parameter CQ MAN 6.2 What are the RFB battery (product) design parameters? Current density; Ion species concentration; Electrode base material; Volume flow rate; Cell size (Product dimensions) lbco:has product design parameter CQ MAN 7.3 What are the control/process parameters (=settings) of the LIB/NIB manufacturing process in BatCAT? Mixing order; Mixing speed; Cutting shape; Cutting size; Calendering speed; Calendering temperature; Calendering thickness; Coating speed; Coating targeted area capacity; Wet coating gap lbco:lib manufacturing process step; lbco:has setting CQ MAN 7.4 What are the control/process parameters (=settings) of the RFB manufacturing process in BatCAT? Furnace temperature; Furnace atmosphere composition; Time at furnace; Cooling time lbco:rfb manufacturing process step; lbco:has setting CQ MAN 8.1 What are the variables to evaluate (=process KPIs) the LIB/NIB manufacturing process? Scrap rate; Production cost per unit lbco:lib manufacturing process; lbco:has process kpi CQ MAN 8.2 What are the variables to evaluate (=process KPIs) the RFB manufacturing process? Production performance; Production cost per unit; Throughput failure rate lbco:rfb manufacturing process; lbco:has process kpi Continued on next page... 15 Table 2 – ... continued from previous page ID Competency Question (CQ) / Natural language sentence (fact) (FA) / Example Query (EQ) Answer [for CQ only] IRI of LBCO most relevant classes/properties CQ MAN 9.1 What are the LIB/NIB battery product main properties (KPIs)? Full cell Initial performance; Full cell Ageing; Full cell Safety lbco:lithium ion battery; lbco:has product kpi CQ MAN 9.2 What are the RFB battery product main properties (KPIs)? Full cell efficiency; Full cell Capacity; Full cell Durability lbco:redox flow battery; lbco:has product kpi CQ MAN 10 What are other general input parameters (settings) of the manufacturing process? Electricity unit cost; Personnel unit cost lbco:manufacturing process; lbco:has setting CQ SIM 6 What is the range of applicability of a given Model (Digital Twin)? n/a - case specific lbco:has applicability range info CQ SIM 9 What type of reproducibility tests are available in general for modelling data? Varying modelling approach; Varying numerical method; varying software lbco:model reproducibility method FA SIM 1 Neural network models can be classified by the dimensionality of input and output fed to the neural network n/a - fact lbco:has nn input dimensionality; lbco:has nn output dimensionality; lbco:neural network model EQ SIM 2 What data was used to define this model parametrization? n/a - answer will be case specific lbco:model parametrization process; lbco:has input EQ SIM 3 What data was used to validate this model parametrization? n/a - answer will be case specific lbco:model validation process; lbco:has input Table 2: Requirements for LBCO expressed in the form of competency question, natural language sentence (“fact”) and example query. This list matches that available on LBCO Github on 16 December 2025. 16 B Acronyms We list in the following main acronyms used throughout the text. ASP: Answer Set Programming; CQ: Competency Question; EMMO: Elementary Multiperspective Material Ontology; GPO: General Process Ontology; IRI: Internationalized Resource Identifier; KPI: Key performance indicator; LBCO: Lightweight BatCAT Core Ontologies ; LiB: Lithium-ion battery; NiB: Sodium-ion battery; OWL: Web Ontology Language; RFB: Redox-flow battery; TTL: Terse Triple Language (Turtle). References [1] M. Alviano, C. Dodaro, J. Marques-Silva, and F. Ricca. Optimum stable model search: Algorithms and implementation. Journal of Logic and Computation, 30(4):863–897, 2020. [2] Claudio Barros, Rebecca Salles, Eduardo Ogasawara, Giancarlo Guizzardi, and Fabio Porto. Requirements for an Ontology of Digital Twins. CEUR workshop proceedings, 2941, 2021. Workshop on Ontology-Driven Conceptual Modelling of Digital Twins, Semantics P and Ds 2021. [3] G. Brewka, T. Eiter, and M. Truszczy´nski. Answer set programming at a glance. Communications of the ACM, 54(12):92–103, 2011. [4] CEN. Materials characterization - Terminology and structured documentation. https://www.cencenelec.eu/media/CEN-CENELEC/CWAs/RI/2025/ cwa17815_2025.pdf, 2025. (Accessed: 25 May 2025). [5] Serge Ch´avez-Feria, Ra´ul Garc´ıa-Castro, and Mar´ıa Poveda-Villal´on. Converting UML-Based Ontology Conceptualizations to OWL with Chowlk. In Ruben Verborgh, Anastasia Dimou, Aidan Hogan, Claudia d’Amato, Ilaria Tiddi, Arne Br¨oring, Simon Mayer, Femke Ongenae, Riccardo Tommasini, and Mehwish Alam, editors, The Semantic Web: ESWC 2021 Satellite Events, Lecture Notes in Computer Science, pages 44–48, Cham, 2021. Springer International Publishing. [6] Silvia Chiacchiera et al. BatCAT Deliverable 4.1: Data landscape and infrastructure related requirements. Technical report, BatCAT Consortium, 2024. To appear on Zenodo. [7] Silvia Chiacchiera et al. Lightweight BatCAT Core Ontology (LBCO) - GitHub repository. https://github.com/HE-BatCAT/lbco, 2025. [8] Simon Clark, Francesca L. Bleken, Simon Stier, Eibar Flores, Casper Welzel Andersen, Marek Marcinek, Anna Szczesna-Chrzan, Miran Gaberscek, M. Rosa Palacin, Martin Uhrin, and Jesper Friis. Toward a Unified Description of Battery Data. Advanced Energy Materials, 12(17):2102702, 2022. Number: 17. 17 [9] E. Erdem, M. Gelfond, and N. Leone. Applications of ASP. AI Magazine, 37(3):53–68, 2016. [10] A. Falkner, G. Friedrich, K. Schekotihin, R. Taupe, and E. Teppan. Industrial applications of answer set programming. K¨unstliche Intelligenz, 32(2-3):165–176, 2018. [11] Daniel Garijo, Oscar Corcho, and Marıa Poveda-Villal´on. FOOPS!: An ontology pitfall scanner for the fair principles. In International Semantic Web Conference (ISWC) 2021: Posters, Demos, and Industry Tracks, volume 2980 of CEUR Workshop Proceedings. CEUR-WS.org, 2021. [12] Martin Thomas Horsch, Silvia Chiacchiera, Welchy Leite Cavalcanti, and Bj¨orn Schembera. Data Technology in Materials Modelling. SpringerBriefs in Applied Sciences and Technology. Springer International Publishing, Cham, 2021. [13] Martin Thomas Horsch, Dmytro Romanov, Eirik Valseth, Salim Belouettar, Luis Eduardo C´ordova L´opez, Johanna Glutting, Mathijs A. Janssen, Peter Klein, Andreas Linhart, Michael A. Seaton, Elin Dypvik Sødahl, Noel Vizcaino, Stephan Werth, Simon Stephan, Ilian T. Todorov, Silvia Chiacchiera, and Fadi Al Machot. Battery manufacturing knowledge infrastructure requirements for multicriteria optimization based decision support in design of simulation. In Proceedings of the First International Workshop on Semantic Materials Science, pages 112–119. CEUR-WS, 2024. [14] ISO/IEC. ISO/IEC 30173:2023: Digital twin — Concepts and terminology. https://www.iso.org/standard/81442.html, 2023. [15] Mark A. Musen. The Prot´eg´e Project: A Look Back and a Look Forward. AI matters, 1(4):4–12, June 2015. [16] Pierluigi Del Nostro, Gerhard Goldbeck, and Daniele Toti. CHAMEO: An ontology for the harmonisation of materials characterisation methodologies. Applied Ontology, 17(3):401–421, 2022. [17] Mar´ıa Poveda-Villal´on, Alba Fern´andez-Izquierdo, Mariano Fern´andezL´opez, and Ra´ul Garc´ıa-Castro. LOT: An industrial oriented ontology engineering framework. Engineering Applications of Artificial Intelligence, 111:104755, 2022. [18] Mar´ıa Poveda-Villal´on, Asunci´on G´omez-P´erez, and Mari Carmen Su´arezFigueroa. OOPS! (OntOlogy Pitfall Scanner!): An On-line Tool for Ontology Evaluation. International Journal on Semantic Web and Information Systems, 10(2):7–34, 2014. [19] W3C. Semantic Sensor Network Ontology. https://www.w3.org/TR/ 2017/REC-vocab-ssn-20171019/, 2017. 18