Semantic Data Enrichment: from Interactive Exploration to Scalable Deployment

OBJECTIVES

• Provide evidence for the hypothesis that several semantic approaches can play an important role in supporting high-quality, controllable, scalable tabular data enrichment processes. This objective is related to proposing data enrichment as a key perspective on semantic solutions to support AI-based applications and intercept a strong demand from the market. Examples of data enrichment tasks and their value in business contexts will be provided.

• Present a link & extend paradigm for data enrichment as a unifying abstraction to develop semantic data enrichment solutions.

• Present key aspects of semantic data enrichment for tabular data, with a particular focus on:

  • The different phases in the design and deployment of data enrichment pipelines, from discovery to scalable deployment, and the role of algorithms, interactive tools, and web-based infrastructure in both phases;
  • The role of semantic table annotation and other NLP-based annotations by discussing existing solutions and their limitations;
  • The role of data pipelines in meeting scalability, repeatability, and engineering constraints;
  • Reasons for which semantic data enrichment is still relevant in the era of generative AI.

• Discuss open research questions to stimulate further research on data enrichment.

• Provide a practical methodology to create semantic data enrichment pipelines for real-world data enrichment tasks, by combining tools that support interactive data exploration and task definition and tools that support scalable deployment of the pipelines.