Philip Stroemert | Searcharxiv

arXiv2026

ChemDCAT-AP: Enabling Semantic Interoperability with a Contextual Extension of DCAT-AP

Cross-domain data integration drives interdisciplinary data reuse and knowledge transfer across domains. However, each discipline maintains its own metadata schemas and domain ontologies, employing distinct conceptual models and application profiles, which complicates semantic interoperability. The W3C Data Catalog Vocabulary (DCAT) offers a widely adopted RDF vocabulary for describing datasets and their distributions, but its core model is intentionally lightweight. Numerous domain-specific application profiles have emerged to enrich DCAT's expressivity, the most well-known DCAT-AP for public data. To facilitate cross-domain interoperability for research data, we propose DCAT-AP PLUS, a DCAT Application Profile (P)roviding additional (L)inks to (U)se-case (S)pecific context (DCAT-AP+). This generic application profile enables a comprehensive representation of the provenance and context of research data generation. DACT-AP+ introduces an upper-level layer that can be specialized by individual domains without sacrificing compatibility. We demonstrate the application of DCAT-AP+ and a specific profile ChemDCAT-AP to showcase the potential of data integration of the neighboring disciplines chemistry and catalysis. We adopt LinkML, a YAML-based modeling framework, to support schema inheritance, generate domain-specific subschemas, and provide mechanisms for data type harmonization, validation, and format conversion, ensuring smooth integration of DCAT-AP+ and ChemDCAT-AP within existing data infrastructures.

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arXiv2024

Rosetta Statements: Simplifying FAIR Knowledge Graph Construction with a User-Centered Approach

Machines need data and metadata to be machine-actionable and FAIR (findable, accessible, interoperable, reusable) to manage increasing data volumes. Knowledge graphs and ontologies are key to this, but their use is hampered by high access barriers due to required prior knowledge in semantics and data modelling. The Rosetta Statement approach proposes modeling English natural language statements instead of a mind-independent reality. We propose a metamodel for creating semantic schema patterns for simple statement types. The approach supports versioning of statements and provides a detailed editing history. Each Rosetta Statement pattern has a dynamic label for displaying statements as natural language sentences. Implemented in the Open Research Knowledge Graph (ORKG) as a use case, this approach allows domain experts to define data schema patterns without needing semantic knowledge. Future plans include combining Rosetta Statements with semantic units to organize ORKG into meaningful subgraphs, improving usability. A search interface for querying statements without needing SPARQL or Cypher knowledge is also planned, along with tools for data entry and display using Large Language Models. The Rosetta Statement metamodel supports a three-step knowledge graph construction procedure. Domain experts can model semantic content without support from ontology engineers by using Wikidata, lowering entry barriers and increasing cognitive interoperability. The second level involves mapping Wikidata terms to established ontologies, and the third step developing semantic graph patterns for reasoning, requiring collaboration with ontology engineers.

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