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.

数据湖系统中的语义数据管理及可扩展性对大数据具有挑战，本研究回顾了最近的方法，重点关注数据湖系统内的应用，涉及基本语义数据管理、丰富数据湖中元数据的语义建模方法和基于本体的数据访问方法，同时指出未来工作中需要更紧密地集成大数据和语义 Web 技术。