Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

Abstract

In this paper we present the Simple-ML framework that we develop to support efficient configuration, robustness and reusability of data analytics workflows through the adoption of semantic technologies. We present semantic data models that lay the foundation for the framework development and discuss the data analytics workflows based on these models. Furthermore, we present an example instantiation of the Simple-ML data models for a real-world use case in the mobility domain.

Tab2KG: Semantic table interpretation with lightweight semantic profiles

“…In the case of domain profiles, the existing mapping to the domain ontology can be modeled by connecting attributes to their corresponding classes, and data type properties [17].…”

“…Lightweight semantic profiles generated by Tab2KG can be utilized as a compact domain and dataset representation to complement and enrich existing dataset catalogs. Such profiles can be generated automatically from the existing datasets and described using the DCAT 17 and the SEAS 18 vocabularies to facilitate their reusability. We believe that lightweight semantic profiles presented in this article are an essential contribution that can benefit a wide range of semantic applications beyond semantic table interpretation.…”

“…Typically, this data does not possess any machine-readable semantics. Semantic table interpretation is an essential step to make this data usable for a wide variety of applications, with data analytics workflows (DAWs) as a prominent example [17]. DAWs include data mining algorithms and sophisticated deep learning architectures, and require a large amount of heterogeneous data as an input.…”

“…In the context of DAWs, semantic table interpretation can bring several advantages. First, an abstraction from tabular data to semantic concepts and relations can guide domain experts in the DAW creation [17]. Second, validation options (e.g., type inference) that become available through the semantic table interpretation can increase the robustness of DAWs [19].…”

Achieving guidance in applied machine learning through software engineering techniques

“…The ML-specific metadata will be complemented by metadata about available datasets. Each dataset will be associated with a semantic description of its features [6]. For each feature, we will store metainformation like its semantic category (e.g.…”