PUMA publications for /group/researchcode/metadata%20interoperabilityFri Jul 26 09:54:41 CEST 2024InteroperAble Descriptions of Observable Property Terminologies (I-ADOPT)2022metadata ontologie interoperability variable Fri Jul 26 09:27:23 CEST 2024Data Science JournalHarmonizing GCW Cryosphere Vocabularies with ENVO and SWEET. Towards a General Model for Semantic Harmonization232024metadata ontologie interoperability semanticWeb Fri Jul 26 09:14:23 CEST 2024WorldFAIR (D2.3) Cross-Domain Interoperability Framework (CDIF) (Report Synthesising Recommendations for Disciplines and Cross-Disciplinary Research Areas)2024forschungsdaten metadata schemaOrg interoperability Wed Jun 19 08:47:39 CEST 2024New York, NY, USAACM Comput. Surv.0481–35Semantic Data Integration and Querying: A Survey and Challenges562024forschungsdaten metadata interoperability semanticWeb integration 26Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused, since it semantically links data allowing wider insights and decision-making. More than thirty works are reviewed. The goal is to help analysts to identify relevant criteria to compare then choose among semantic data integration approaches, focusing on the category (materialized, virtual or hybrid) and querying techniques.Mon Jun 17 11:53:44 CEST 2024Materials & Design112603PMD Core Ontology: Achieving semantic interoperability in materials science2372024forschungsdaten metadata interoperability materialScience ontology Knowledge representation in the Materials Science and Engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant (consistent) and variant (context-specific) knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific. In this paper, an approach how to maintain a comprehensive MSE-centric terminology composing a mid-level ontology–the Platform MaterialDigital Core Ontology (PMDco)–via MSE community-based curation procedures is presented. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics. Additionally, it demonstrates how the PMDco lowers development and integration thresholds. Moreover, the research highlights how to fuel it with real-world data sources ranging from manually conducted experiments and simulations with continuously automated industrial applications.Mon Sep 12 14:59:13 CEST 2022ACM Comput. Surv.0227:1-7:37A survey of techniques for achieving metadata interoperability422010forschungsdaten metadata interoperability