Capturing and anticipating user intents in data analytics via knowledge graphs

Abstract

In today’s data-driven world, the ability to extract meaningful information from data is becoming essential for businesses, organizations and researchers. For this purpose, a wide range of tools and systems exists addressing data-related tasks, from data integration, preprocessing and modeling, to the interpretation and evaluation of the results. As data continues to grow in volume and complexity, there is an increasing need for advanced yet user-friendly tools, such as intelligent discovery assistants (IDAs) or automated machine learning (AutoML) systems, that facilitate the user’s interaction with data. This enables non-expert users to effectively leverage powerful data analytics techniques. However, the use of these tools still requires non-trivial user input that cannot be anticipated from the analytical problem’s data alone, but must be tailored to each individual user and their specific intents. To this end, this work explores the use of Knowledge Graphs (KG) as a foundational representation for capturing complex analytics workflows, as well as information about the users, their intents and their feedback, in order to facilitate user interaction with IDAs or AutoML tools. This is achieved through established techniques from recommender systems, in particular a link prediction approach based on KG embeddings and graph neural networks. Experimental results show that the proposed method effectively captures the graph structure and produces meaningful suggestions for users. To demonstrate the feasibility of the approach, a working prototype is presented.

This work is supported by the Horizon Europe Programme under GA.101093164 (ExtremeXP) and the Spanish Ministerio de Ciencia e Innovación under project PID2023-152841OAI00/AEI/10.13039/501100011033 (TALC).

Peer Reviewed

Postprint (published version)