Automatic Table completion using Knowledge Base

TL;DR

This paper introduces AutoTableComplete, a framework that automatically completes relational tables from natural language queries and example rows by leveraging a heterogeneous knowledge base, improving data gathering efficiency.

Contribution

The work presents a novel framework that combines schema information and natural language context to automatically fill tables using knowledge bases, along with a new dataset for evaluation.

Findings

AutoTableComplete effectively completes tables with relevant entity rows.

The framework outperforms baseline methods in accuracy and relevance.

A new dataset based on Wikipedia and Freebase supports extensive evaluation.

Abstract

Table is a popular data format to organize and present relational information. Users often have to manually compose tables when gathering their desiderate information (e.g., entities and their attributes) for decision making. In this work, we propose to resolve a new type of heterogeneous query viz: tabular query, which contains a natural language query description, column names of the desired table, and an example row. We aim to acquire more entity tuples (rows) and automatically fill the table specified by the tabular query. We design a novel framework AutoTableComplete which aims to integrate schema specific structural information with the natural language contextual information provided by the user, to complete tables automatically, using a heterogeneous knowledge base (KB) as the main information source. Given a tabular query as input, our framework first constructs a set of candidate chains that connect the given example entities in KB. We learn to select the best matching chain from these candidates using the semantic context from tabular query. The selected chain is then converted into a SPARQL query, executed against KB to gather a set of candidate rows, that are then ranked in order of their relevance to the tabular query, to complete the desired table. We construct a new dataset based on tables in Wikipedia pages and Freebase, using which we perform a wide range of experiments to demonstrate the effectiveness of AutoTableComplete as well as present a detailed error analysis of our method.