Semantic Data Management in Data Lakes

In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontologybased data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies

Metadata4Ing: An ontology for describing the generation of research data within a scientific activity

The ontology Metadata4Ing is developed within the NFDI Consortium NFDI4Ing with the aim of providing a thorough framework for the semantic description of research data, with a particular focus on engineering sciences and neighbouring disciplines. This ontology allows a thorough description of the whole data generation process (experiment, observation, simulation), embracing the object of investigation, all sample and data manipulation procedures, a summary of the data files and the information contained, and all personal and institutional roles. The subordinate classes and relations can be built according to the two principles of inheritance and modularity. Inheritance means that a subclass inherits all properties of its superordinate class, possibly adding some new ones. Modularity means that all expansions are independent of each other; this makes possible for instance to generate expanded ontologies for any possible combinations of method × object of research

Metadata4Ing: An ontology for describing the generation of research data within a scientific activity.

Arndt S, Farnbacher B, Fuhrmans M, et al. Metadata4Ing: An ontology for describing the generation of research data within a scientific activity.The ontology Metadata4Ing is developed within the NFDI Consortium NFDI4Ing with the aim of providing a thorough framework for the semantic description of research data, with a particular focus on engineering sciences and neighbouring disciplines. This ontology allows a thorough description of the whole data generation process (experiment, observation, simulation), embracing the object of investigation, all sample and data manipulation procedures, a summary of the data files and the information contained, and all personal and institutional roles. The subordinate classes and relations can be built according to the two principles of inheritance&nbsp;and modularity. Inheritance&nbsp;means that a subclass inherits all properties of its superordinate class, possibly adding some new ones. Modularity&nbsp;means that all expansions are independent of each other; this makes possible for instance to generate expanded ontologies for any possible combinations of method &times; object of research. The new version 1.2.0 simplifies the specification of parameters by variables Allows to assign string values to variables Offers the possibility to model value ranges uses qudt terms for unit and quantity kind switches to prov:Agent and prov:Organisation (replacing foaf:Person and foaf:Organisation) updates&nbsp;JSON-LD context file, the documentation, the README and the metadata of the ontology adds a citation.cff file </ul