A Multi-Level Architecture for Reusable Materials Ontologies -- The OntoCrafter Ceramics Ontology (OCO) as Reference Implementation

22d ago · Global · primary source: export.arxiv.org

A multi-level architecture designed to unify fragmented materials-science ontologies has been proposed, with the OntoCrafter Ceramics Ontology (OCO) serving as its reference implementation, according to a paper posted on arXiv [1]. The materials-science ontology landscape is fractured along multiple axes. A recent survey identified 94 ontologies, of which more than 40 are structurally incompatible, and each new application domain typically restarts ontology design from scratch [1]. At the same time, European Union regulations — including CSRD, CSDDD, PPWR, CBAM, R2R, the AI Act, and ESPR — require material, manufacturing, supply-chain, and lifecycle data to be integrated into digital product passports, a demand that horizontally fragmented ontologies cannot meet [1]. The paper also notes that existing vocabularies often record a fact — such as BNT-BT having a d33 of approximately 580 pC/N — without surfacing the underlying mechanistic explanation, which involves Bi-6s² lone-pair stereo-activity, anomalous Born effective charges, soft modes, and defect chemistry [1]. The proposed architecture introduces two independent classification axes: level of abstraction and consumer audience. The abstraction axis spans four levels, from L0 bridges to L3 categorical reasoning, while the audience axis distinguishes between material and compliance consumers [1]. At the material-specific level, the design is internally organized by a seven-tier mechanistic-explanation skeleton — Symmetry, Energy/DFT, Thermo/CALPHAD, Kinetics, Microstructure, Defect chemistry, and Bonding — applicable to any crystalline ionic oxide [1]. The reference implementation, OCO v0.94, contains 5,196 classes across 44 modules, 167,348 OWL axioms of which 40,454 are logical, 1,674 properties, 829 cross-ontology bridge mappings, 1,172 SHACL shapes, and 163 published competency questions [1]. The paper was submitted to arXiv on 12 June 2026 under the condensed-matter materials-science category [1]. arXiv, which began on 14 August 1991, is an open-access repository of electronic preprints that is not peer-reviewed and has grown to a submission rate of about 24,000 articles per month as of November 2024 [6]. The platform’s arXivLabs framework, launched in 2020, allows community collaborators to develop experimental tools that appear on article record pages, though new proposals were paused while the development team focused on modernizing arXiv’s systems and moving them to the cloud [3][4].

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Background sources we checked (7)
  • arxiv.org ↗ The Materials Science and Engineering ontology landscape is fragmented along multiple axes simultaneously. Horizontally: a recent survey identified 94 ontologies of which over 40 are structurally incompatible; each new application domain -- ceramics, polymers, batteries, smart ma…
  • info.arxiv.org ↗ arXiv Labs - arXiv info | arXiv e-print repository Skip to content # arXiv Labs Attention arXiv Users: arXiv Labs is pausing new proposals ## What are arXiv Labs? arXiv Labs are a way for the community to contribute new, useful features to arXiv. These integrations are avail…
  • blog.arxiv.org ↗ arXivLabs: a space for community innovation – arXiv blog arXiv has launched a new, formalized framework enabling innovative collaborations with individuals and organizations. “Members of our community want to contribute tools that enhance the arXiv experience, and we val…
  • info.arxiv.org ↗ arXivLabs: Showcase - arXiv info | arXiv e-print repository ... # arXivLabs: Showcase ... arXiv is surrounded by a community of researchers and developers working at the cutting edge of information science and technology. ... While the arXiv team is focused on our core mission—pr…
  • en.wikipedia.org ↗ arXiv (pronounced as "archive"—the X represents the Greek letter chi ⟨χ⟩) is an open-access repository of electronic preprints and postprints (known as e-prints) approved for posting after moderation, but not peer reviewed. It consists of scientific papers in the fields of mathem…
  • en.wikipedia.org ↗ 14 (fourteen) is the natural number following 13 and preceding 15.…
  • en.wikipedia.org ↗ A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.…

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