Recommending Semantic Concepts for Improving the Process of Semantic Modeling

Abstract

Data lakes offer enterprises an easy-to-use approach for centralizing the collection of their data sets. However, by just filling the data lake with raw data sets, the probability of creating a data swamp increases. To overcome this drawback, the annotation of data sets with additional meta information is crucial. One way to provide data with such information is to use semantic models that enable the automatic interpretation and processing of data values and their context. However, creating semantic models for data sets containing hundreds of data attributes requires a lot of effort. To support this modeling process, external knowledge bases provide the background knowledge required to create sophisticated semantic models.

In order to benefit from this existing knowledge, we propose a novel modular recommendation framework for identifying the best fitting semantic concepts for a set of data attribute labels. The framework, whose design is based on intensive review of real-world data attribute labels, queries arbitrary pluggable knowledge bases and weights/aggregates their results. We evaluate our approach with different existing knowledge bases and compare it with existing state-of-the-art approaches. In addition, we integrate it into the semantic data platform ESKAPE and discuss how it simplifies the process of creating semantic models.