# KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science

arXiv: https://arxiv.org/abs/2303.02204 · HTML: https://mlsanity.com/paper/2303.02204/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 66 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 22 of 22)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions](https://mlsanity.com/paper/2109.12843/) (2021)
- [TABBIE: Pretrained Representations of Tabular Data](https://mlsanity.com/paper/2105.02584/) (2021)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [Semi-supervised sequence tagging with bidirectional language models](https://mlsanity.com/paper/1705.00108/) (2017)
- [HoloClean: Holistic Data Repairs with Probabilistic Inference](https://mlsanity.com/paper/1702.00820/) (2017)
- [Sherlock: A Deep Learning Approach to Semantic Data Type Detection](https://mlsanity.com/paper/1905.10688/) (2019)
- [Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning](https://mlsanity.com/paper/2210.01922/) (2022)
- [SANTOS: Relationship-based Semantic Table Union Search](https://mlsanity.com/paper/2209.13589/) (2022)
- [Dataset Discovery in Data Lakes](https://mlsanity.com/paper/2011.10427/) (2020)
- [A Toolkit for Generating Code Knowledge Graphs](https://mlsanity.com/paper/2002.09440/) (2020)
- [PyCG: Practical Call Graph Generation in Python](https://mlsanity.com/paper/2103.00587/) (2021)
- [Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search](https://mlsanity.com/paper/2106.13861/) (2021)
- [DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python](https://mlsanity.com/paper/2104.00841/) (2021)
- [A Scalable AutoML Approach Based on Graph Neural Networks](https://mlsanity.com/paper/2111.00083/) (2021)
- [The Odyssey Approach for Optimizing Federated SPARQL Queries](https://mlsanity.com/paper/1705.06135/) (2017)
- [Recognizing Variables from their Data via Deep Embeddings of Distributions](https://mlsanity.com/paper/1909.04844/) (2019)
- [Federated Data Science to Break Down Silos [Vision]](https://mlsanity.com/paper/2111.13186/) (2021)

## Built on by (most cited first, 2 of 2)

- [Generative Benchmark Creation for Table Union Search](https://mlsanity.com/paper/2308.03883/) (2023)
- [Discovery-Driven Integration of Disjoint Tables via Text](https://mlsanity.com/paper/2609.26658/) (2026)

## Abstract (arXiv)

In recent years, we have witnessed the growing interest from academia and industry in applying data science technologies to analyze large amounts of data. In this process, a myriad of artifacts (datasets, pipeline scripts, etc.) are created. However, there has been no systematic attempt to holistically collect and exploit all the knowledge and experiences that are implicitly contained in those artifacts. Instead, data scientists recover information and expertise from colleagues or learn via trial and error. Hence, this paper presents a scalable platform, KGLiDS, that employs machine learning and knowledge graph technologies to abstract and capture the semantics of data science artifacts and their connections. Based on this information, KGLiDS enables various downstream applications, such as data discovery and pipeline automation. Our comprehensive evaluation covers use cases in data discovery, data cleaning, transformation, and AutoML. It shows that KGLiDS is significantly faster with a lower memory footprint than the state-of-the-art systems while achieving comparable or better accuracy.