# Semantic Data Management in Data Lakes

arXiv: https://arxiv.org/abs/2310.15373 · HTML: https://mlsanity.com/paper/2310.15373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 141 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Annotating Columns with Pre-trained Language Models](https://mlsanity.com/paper/2104.01785/) (2021)
- [Column Type Annotation using ChatGPT](https://mlsanity.com/paper/2306.00745/) (2023)
- [Data Lakes: A Survey of Functions and Systems](https://mlsanity.com/paper/2106.09592/) (2021)
- [SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs](https://mlsanity.com/paper/2008.07176/) (2020)
- [MTab: Matching Tabular Data to Knowledge Graph using Probability Models](https://mlsanity.com/paper/1910.00246/) (2019)
- [Learning the Semantics of Structured Data Sources](https://mlsanity.com/paper/1601.04105/) (2016)
- [The Query Translation Landscape: a Survey](https://mlsanity.com/paper/1910.03118/) (2019)
- [Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles](https://mlsanity.com/paper/2302.01150/) (2023)
- [Optimizing Federated Queries Based on the Physical Design of a Data Lake](https://mlsanity.com/paper/2002.08102/) (2020)
- [Coining goldMEDAL: A New Contribution to Data Lake Generic Metadata Modeling](https://mlsanity.com/paper/2103.13155/) (2021)

## Abstract (arXiv)

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.