Auto-completion for Data Cells in Relational Tables

Abstract

We address the task of auto-completing data cells in relational tables. Such tables describe entities (in rows) with their attributes (in columns). We present the CellAutoComplete framework to tackle several novel aspects of this problem, including: (i) enabling a cell to have multiple, possibly conflicting values, (ii) supplementing the predicted values with supporting evidence, (iii) combining evidence from multiple sources, and (iv) handling the case where a cell should be left empty. Our framework makes use of a large table corpus and a knowledge base as data sources, and consists of preprocessing, candidate value finding, and value ranking components. Using a purpose-built test collection, we show that our approach is 40\% more effective than the best baseline.

In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM '19), 2019

References in corpus (3)

• Ad Hoc Table Retrieval using Semantic Similarity
• EntiTables: Smart Assistance for Entity-Focused Tables
• On-the-fly Table Generation

Cited by in corpus (6)

• StruBERT: Structure-aware BERT for Table Search and Matching
• Novel Entity Discovery from Web Tables
• Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods
• Examining Autocompletion as a Basic Concept for Interaction with Generative AI
• Semantic Table Retrieval using Keyword and Table Queries
• Generating Categories for Sets of Entities