Relational and noSQL storages are developed for the fast processing of the large data sets having a stable structure, while the ontologies are used to rep-resent complex and dynamic sets of information of a limited size. In the in-dustrial applications it is often needed to maintain the large warehouses of data consolidated from various sources. The ontologies are useful to repre-sent the structure of that data, but RDF triple stores are not well suitable for storing it. We offer an approach and a system allowing to use the opportuni-ties of fast storage engines along with the flexibility of ontology-based data management tools, including SPARQL queries. The system implements a multi-model data abstraction layer which allows working with the data as if it is situated in RDF triple store, executes SPARQL queries over it and ap-plies SHACL constraints and rules.
Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping
Bosch Center for Artificial Intelligence（博世人工智能中心）；Oslo Metropolitan University（奥斯陆城市大学）；University of Oslo（奥斯陆大学）；Nanjing University（南京大学）；City, University of London（伦敦城市大学）
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
University of Oxford（牛津大学）；Athens University of Economics and Business（雅典经济与商业大学）；University of Athens（雅典大学）；University of Lübeck（吕贝克大学）；Siemens Corporate Technology（西门子中央研究院）
Interactive Ontology Matching with Cost-Efficient Learning
NEC Laboratories Europe（欧洲 NEC 实验室）；Springer Nature（施普林格·自然）；Zurich University of Applied Sciences（苏黎世应用科技大学）；Universidad de Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）
SSBD Ontology: A Two-Tier Approach for Interoperable Bioimaging Metadata
RIKEN Information R&D and Strategy Headquarters（日本理化学研究所信息研发与战略本部）；RIKEN Bioresource Research Center（日本理化学研究所生物资源研究中心）；RIKEN Center for Biosystems Dynamics Research（日本理化学研究所生物系统动力学研究中心）
