Towards Semantically Enhanced Data Understanding

TL;DR

This paper proposes a semantic model linking data with its documentation to improve data understanding and reduce lookup overhead, demonstrated through an early prototype.

Contribution

It introduces a unified semantic model that interlinks data and documentation, enabling easier access, browsing, and supporting various data analysis tasks.

Findings

Semantic model effectively links data with documentation

Prototype demonstrates improved data understanding workflow

Supports searching, comparing, and visualizing data

Abstract

In the field of machine learning, data understanding is the practice of getting initial insights in unknown datasets. Such knowledge-intensive tasks require a lot of documentation, which is necessary for data scientists to grasp the meaning of the data. Usually, documentation is separate from the data in various external documents, diagrams, spreadsheets and tools which causes considerable look up overhead. Moreover, other supporting applications are not able to consume and utilize such unstructured data. That is why we propose a methodology that uses a single semantic model that interlinks data with its documentation. Hence, data scientists are able to directly look up the connected information about the data by simply following links. Equally, they can browse the documentation which always refers to the data. Furthermore, the model can be used by other approaches providing additional support, like searching, comparing, integrating or visualizing data. To showcase our approach we also demonstrate an early prototype.