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Papers listed here may be not from top publications, some of them even are not for purely relational data, but are all interesting papers related to relational data augmentation that deserve reading.

[SIGMOD] SANTOS: Relationship-based Semantic Table Union Searchtaset Discovery from Data Lakes with Contextualized Column-based Representation Learning [paper] [official code]

[VLDB] Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning [paper] [official code]

[ACL findings] Automatic Table Union Search with Tabular Representation Learning [paper]

[VLDB] DeepJoin: Joinable Table Discovery with Pre-Trained Language Models [paper]

[SIGMOD Conference Companion] Table Discovery in Data Lakes: State-of-the-art and Future Directions [paper]

[SIGMOD] Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation [paper]

[VLDB] Integrating Data Lake Tables [paper] [official code]

[SIGMOD] Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation [paper]

[VLDB] MATE: multi-attribute table extraction [paper] [official code]

[VLDB] Selective data acquisition in the wild for model charging [paper]

[ICDE] Feature Augmentation with Reinforcement Learning [paper]

[VLDB] Coresets over multiple tables for feature-rich and data-efficient machine learning [paper] [official code]

[ICDE] A Sketch-based Index for Correlated Dataset Search [paper]

[TKDE] Data Lake Organization [paper]

[SIGMOD] Annotating Columns with Pre-trained Language Models [paper] [official code]

[WWW] StruBERT: Structure-aware BERT for Table Search and Matching [paper] [official code]

[SIGMOD] Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and Beyond [paper] [official code]

[ICDE] Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach [paper]

[VLDB] Automatic data acquisition for deep learning [paper]

[ICDE] Valentine: Evaluating Matching Techniques for Dataset Discovery [paper] [official code]

[VLDB] RONIN: data lake exploration [paper]

[VLDB] ARDA: automatic relational data augmentation for machine learning [paper]

[ICDE] Dataset Discovery in Data Lakes [paper]

[VLDB] Relational data synthesis using generative adversarial networks: a design space exploration [paper] [official code]

[VLDB] Sato: contextual semantic type detection in tables [paper] [official code]

[VLDB] TURL: table understanding through representation learning [paper] [official code]

[WWW] Novel Entity Discovery from Web Tables [paper]

[SIGMOD] Organizing Data Lakes for Navigation [paper]

[SIGMOD] JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes [paper]

[IJCAI] FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data [paper]

[SIGIR] Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval [paper] [official code]

[SIGKDD] Sherlock: A Deep Learning Approach to Semantic Data Type Detection [paper]

[CIKM] Auto-completion for Data Cells in Relational Tables [paper]

[VLDB] Table union search on open data [paper]

[VLDB] Open data integration [paper]

[ICDE] Aurum: A Data Discovery System [paper] [official code]

[VLDB] Data synthesis based on generative adversarial networks [paper]

[SIGIR] EntiTables: Smart Assistance for Entity-Focused Tables [paper]

[VLDB] LSH ensemble: internet-scale domain search [paper]

[BDC] Towards a Hybrid Imputation Approach Using Web Tables [paper]

[SIGMOD] InfoGather+: semantic matching and annotation of numeric and time-varying attributes in web tables [paper]

[SIGMOD] InfoGather: entity augmentation and attribute discovery by holistic matching with web tables [paper]

[SIGMOD] Finding related tables [paper]