# Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs

arXiv: https://arxiv.org/abs/2411.01023 · HTML: https://mlsanity.com/paper/2411.01023/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 55 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 20 of 20)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering](https://mlsanity.com/paper/2305.03403/) (2023)
- [Knowledge Graph Completion via Complex Tensor Factorization](https://mlsanity.com/paper/1702.06879/) (2017)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks](https://mlsanity.com/paper/2306.08107/) (2023)
- [Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs](https://mlsanity.com/paper/2211.15845/) (2022)
- [Automating Data Science: Prospects and Challenges](https://mlsanity.com/paper/2105.05699/) (2021)
- [Towards Continual Knowledge Graph Embedding via Incremental Distillation](https://mlsanity.com/paper/2405.04453/) (2024)
- [Causal Incremental Graph Convolution for Recommender System Retraining](https://mlsanity.com/paper/2108.06889/) (2021)
- [Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning](https://mlsanity.com/paper/2206.10140/) (2022)
- [Beyond Roll-Up's and Drill-Down's: An Intentional Analytics Model to Reinvent OLAP (long-version)](https://mlsanity.com/paper/1812.07854/) (2018)

## Abstract (arXiv)

In today's data-driven world, the ability to extract meaningful information from data is becoming essential for businesses, organizations and researchers alike. For that purpose, a wide range of tools and systems exist 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, variety, and complexity, there is an increasing need for advanced but user-friendly tools, such as intelligent discovery assistants (IDAs) or automated machine learning (AutoML) systems, that facilitate the user's interaction with the data. This enables non-expert users, such as citizen data scientists, to leverage powerful data analytics techniques effectively. The assistance offered by IDAs or AutoML tools should not be guided only by the analytical problem's data but should also be tailored to each individual user. To this end, this work explores the usage of Knowledge Graphs (KG) as a basic framework for capturing in a human-centered manner complex analytics workflows, by storing information not only about the workflow's components, datasets and algorithms but also about the users, their intents and their feedback, among others. The data stored in the generated KG can then be exploited to provide assistance (e.g., recommendations) to the users interacting with these systems. To accomplish this objective, two methods are explored in this work. Initially, the usage of query templates to extract relevant information from the KG is studied. However, upon identifying its main limitations, the usage of link prediction with knowledge graph embeddings is explored, which enhances flexibility and allows leveraging the entire structure and components of the graph. The experiments show that the proposed method is able to capture the graph's structure and to produce sensible suggestions.