EntiTables: Smart Assistance for Entity-Focused Tables

Abstract

Tables are among the most powerful and practical tools for organizing and working with data. Our motivation is to equip spreadsheet programs with smart assistance capabilities. We concentrate on one particular family of tables, namely, tables with an entity focus. We introduce and focus on two specific tasks: populating rows with additional instances (entities) and populating columns with new headings. We develop generative probabilistic models for both tasks. For estimating the components of these models, we consider a knowledge base as well as a large table corpus. Our experimental evaluation simulates the various stages of the user entering content into an actual table. A detailed analysis of the results shows that the models' components are complimentary and that our methods outperform existing approaches from the literature.

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17), 2017

Cited by in corpus (10)

• Ad Hoc Table Retrieval using Semantic Similarity
• Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval
• StruBERT: Structure-aware BERT for Table Search and Matching
• Auto-completion for Data Cells in Relational Tables
• On-the-fly Table Generation
• Semantic Table Retrieval using Keyword and Table Queries
• Dataset search: a survey
• Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset
• SmartTable: A Spreadsheet Program with Intelligent Assistance
• Generating Categories for Sets of Entities