EntiTables: Smart Assistance for Entity-Focused Tables

TL;DR

EntiTables introduces probabilistic models to assist users in populating entity-focused tables in spreadsheets by suggesting new entities and column headings, leveraging knowledge bases and large table corpora.

Contribution

The paper presents novel generative probabilistic models for table population tasks, outperforming existing methods and integrating knowledge bases with table corpora for smarter spreadsheet assistance.

Findings

Models outperform existing approaches

Components are complementary and effective

Methods successfully simulate real user table editing

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.